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   "source": [
    "from warnings import filterwarnings\n",
    "from pandas import Series\n",
    "import glob\n",
    "import re\n",
    "import pandas as pd\n",
    "import spacy\n",
    "from paddlenlp import Taskflow\n",
    "from gensim.models import Word2Vec\n",
    "import multiprocessing\n",
    "import torch\n",
    "import networkx as nx\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "filterwarnings('ignore', category=DeprecationWarning)"
   ],
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   "execution_count": 8,
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  },
  {
   "cell_type": "code",
   "source": [
    "files = glob.glob(\"../Data/数据清洗/*\")\n",
    "my_stop_word = ['work location', \"requirement\",'job responsibility', 'job description : job summary', 'post', 'company', 'a', 'professional requirement', 'job requirement', 'you', 'recommended', 'experience', 'overview', 'position', 'requirements', 'd', 'location', 'introduction', 'education', 'welfare benefits', 'job requirements', 'job', 'including', 'summary', 'other', 'professional', 'today', 'work address', 'address', 'job summary', 'hour', 'work atmosphere', 'requirement', 'working', 'work', 'job responsibilities', 'include', 'skill requirements', 'employment', 'benefits', 'benefit', 'education requirements', 'category', 'welfare benefit', 'company introduction', 'personality', 'years', 'salary', 'year', 'instructions', 'hours', 'work time', 'instruction', 'daily', 'tomorrow', 'requisition', 'recommend', 'least', 'other instructions', 'responsibilities', 'job description', 'description', 'qualification', 'professional requirements', 'g', 'atmosphere', 'equal', 'recommended skills', 'welfare', 'skill requirement', 'education requirement', 'job benefits', 'salary benefits', 'job benefit', 'work hours', 'skill', 'job qualification', 'at least a', 'responsibility', 'time', 'id', 'responsibilitie', 'role', 'requisition id', ':', 'other instruction', 'at', 'skills', 'daily work'] + [\"岗位\", \"职责\", \"描述\", \"福利\", \"任职要求\", \"工作职责\",\"岗位需求\",\"职责描述\",\"工作地点\",\"技能要求\",\"专业要求\",\"技能要求\",\"学历要求\",\"其他说明\",\"公司介绍\",\"上班地址\",\"上班时间\",\"性格方面\",\"技能方面\",\"经验方面\",\"公司简介\", \"岗位职责\", \"岗位要求\", \"职位福利\", \"福利待遇\",\"任职资格\", \"工作氛围\", \"薪酬福利\", \"任职资格\", \"日常工作\", \"在这里\", \"只要你\"]\n",
    "stop_words1 = open(\"../Data/stop_words.txt\", encoding=\"utf-8\").read().split(\"\\n\")\n",
    "stop_words2 = open(\"../Data/Stopwords/stopwords_full.txt\", encoding=\"utf-8\").read().split(\"\\n\")\n",
    "stop_words3 = open(\"../Data/Stopwords/stopwords_baidu.txt\", encoding=\"utf-8\").read().split(\"\\n\")\n",
    "stop_words4 = open(\"../Data/Stopwords/stopwords_scu.txt\", encoding=\"utf-8\").read().split(\"\\n\")\n",
    "stop_words5 = open(\"../Data/Stopwords/stopwords_hit.txt\", encoding=\"utf-8\").read().split(\"\\n\")\n",
    "stop_words6 = open(\"../Data/Stopwords/stopwords_cn.txt\", encoding=\"utf-8\").read().split(\"\\n\")\n",
    "stop_words7 = open(\"../Data/Stopwords/stopwords_english.txt\", encoding=\"utf-8\").read().split(\"\\n\")\n",
    "stop_words = list(set(stop_words2+stop_words1+stop_words3+stop_words4+stop_words5+stop_words6+stop_words7))\n",
    "    \n",
    "En_data = list()\n",
    "Zh_data = list()\n",
    "for file in files:\n",
    "    file_name = file.replace('\\\\', '/').split('/')[-1].split('.')[0]\n",
    "    print(f\"当前网站: {file_name}\")\n",
    "    df = pd.read_csv(file, index_col=0)\n",
    "    # 删除重复行\n",
    "    df = df.drop_duplicates(keep='last')\n",
    "    # 去掉缺失值\n",
    "    df = df.dropna(subset=['职位', '任职要求'])\n",
    "    if re.search('[\\u4e00-\\u9fa5]', file_name):  # 中文网站\n",
    "        Zh_data += df[\"任职要求\"].tolist()\n",
    "    else:  # 英文网站\n",
    "        En_data += df[\"任职要求\"].tolist()\n",
    "print(\"英文数据:\\t\",len(En_data), \"条数据\")\n",
    "print(\"中文数据:\\t\",len(Zh_data), \"条数据\")"
   ],
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    }
   },
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前网站: boss直聘\n",
      "当前网站: CareerBuilder\n",
      "当前网站: CIA\n",
      "当前网站: DNI\n",
      "当前网站: linkedin\n",
      "当前网站: Simplyhired\n",
      "当前网站: 智联招聘\n",
      "英文数据:\t 1669 条数据\n",
      "中文数据:\t 3434 条数据\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "class ToVec:\n",
    "    en_nlp = spacy.load(\"en_core_web_lg\")\n",
    "    zh_nlp = Taskflow(\"ner\")\n",
    "    model = None\n",
    "    word_vectors = None\n",
    "    clustered_words = None\n",
    "    \n",
    "    def preprocessing(self,text):\n",
    "        pass\n",
    "    \n",
    "    def train(self,data:Series):\n",
    "        corpus = [self.preprocessing(i) for i in data]\n",
    "        print(\"分词结束~\")\n",
    "        # 训练Word2Vec模型 sg=1表示使用Skip-gram，negative=5表示使用负采样，window是上下文窗口的大小，min_count是最小词频阈值，workers是训练时的并行工作数。\n",
    "        print(\"训练模型中~\")\n",
    "        self.model = Word2Vec(corpus, sg=1, negative=5, window=5, vector_size=300, min_count=5, workers=multiprocessing.cpu_count())\n",
    "        self.word_vectors = [self.model.wv[word] for word in self.model.wv.key_to_index]\n",
    "        \n",
    "    def elbow_method_optimized(self, max_k=11):\n",
    "        sse = {}\n",
    "        silhouette_scores = {}\n",
    "    \n",
    "        for k in range(2, max_k):  # 轮廓分数至少需要2个聚类\n",
    "            kmeans = KMeans(n_clusters=k, max_iter=1000).fit(self.word_vectors)\n",
    "            labels = kmeans.labels_\n",
    "            sse[k] = kmeans.inertia_\n",
    "            silhouette_scores[k] = silhouette_score(self.word_vectors, labels)\n",
    "    \n",
    "        # 绘图\n",
    "        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "    \n",
    "        # 手肘法\n",
    "        ax1.plot(list(sse.keys()), list(sse.values()))\n",
    "        ax1.set_xlabel(\"Number of cluster\")\n",
    "        ax1.set_ylabel(\"SSE\")\n",
    "        ax1.set_title(\"Elbow Method\")\n",
    "    \n",
    "        # 轮廓分数\n",
    "        ax2.plot(list(silhouette_scores.keys()), list(silhouette_scores.values()))\n",
    "        ax2.set_xlabel(\"Number of cluster\")\n",
    "        ax2.set_ylabel(\"Silhouette Score\")\n",
    "        ax2.set_title(\"Silhouette Score Method\")\n",
    "        \n",
    "        plt.show()\n",
    "    \n",
    "    # 对词向量进行K-means聚类\n",
    "    def cluster_words_optimized(self, n_clusters):\n",
    "        kmeans = KMeans(n_clusters=n_clusters, max_iter=5000).fit(self.word_vectors)\n",
    "        labels = kmeans.labels_\n",
    "        clustered_words = {}\n",
    "        for i, word in enumerate(self.model.wv.key_to_index):\n",
    "            clustered_words[word] = labels[i]\n",
    "        self.clustered_words = clustered_words\n",
    "\n",
    "    def save_node_edge_optimized(self, path, similarity_threshold=0.9):\n",
    "        word_vectors = self.model.wv\n",
    "        graph = nx.Graph()\n",
    "        # 选取关键词汇并获取聚类标签\n",
    "        selected_words = sorted(word_vectors.key_to_index, key=lambda word: word_vectors.get_vecattr(word, 'count'), reverse=True)\n",
    "        selected_vectors = torch.tensor([word_vectors[word] for word in selected_words])\n",
    "    \n",
    "        # 确保向量在GPU上（如果可用）\n",
    "        if torch.cuda.is_available():\n",
    "            selected_vectors = selected_vectors.to('cuda')\n",
    "    \n",
    "        # 计算余弦相似度\n",
    "        norm_vectors = selected_vectors / selected_vectors.norm(dim=1)[:, None]\n",
    "        similarity_matrix = torch.mm(norm_vectors, norm_vectors.transpose(0, 1))\n",
    "    \n",
    "        # 构建网络图的节点和边\n",
    "        for idx, word in enumerate(selected_words):\n",
    "            # 将聚类标签作为节点属性\n",
    "            graph.add_node(word, cluster=self.clustered_words[word])\n",
    "            # 只考虑高于阈值的相似度\n",
    "            similar_indices = torch.where(similarity_matrix[idx] > similarity_threshold)[0]\n",
    "            for sim_idx in similar_indices:\n",
    "                if sim_idx != idx:  # 排除自身\n",
    "                    similar_word = selected_words[sim_idx]\n",
    "                    similarity = similarity_matrix[idx, sim_idx].item()\n",
    "                    graph.add_edge(word, similar_word, weight=similarity)\n",
    "    \n",
    "        # 保存图\n",
    "        print(\"保存文件中~\", f'{path}.gexf')\n",
    "        nx.write_gexf(graph, f'{path}.gexf')"
   ],
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   "execution_count": 10,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "class EnToVec(ToVec):\n",
    "    def preprocessing(self, text):\n",
    "        doc = self.en_nlp(text)\n",
    "        return [token.lemma_.lower() for token in doc if not token.is_stop and not token.is_punct and len(token.text)>2 and token.text.lower() not in my_stop_word and token.text not in stop_words and not re.findall(\"\\W\",token.text) and not token.like_num]\n",
    "    \n",
    "class ZhToVec(ToVec):\n",
    "    def preprocessing(self, text):\n",
    "        if len(re.findall(\"[A-Za-z]\",text)) / len(re.findall('\\w',text)) > 0.8:\n",
    "            doc = self.en_nlp(text)\n",
    "            return [token.lemma_.lower() for token in doc if not token.is_stop and len(token.text)>2 and token.text.lower() not in my_stop_word and token.text not in stop_words and not re.findall(\"\\W\",token.lemma_.lower()) and not token.like_num]\n",
    "        else:\n",
    "            res = self.zh_nlp(text)\n",
    "            return [i.lower() for i,label in res if i.lower() not in my_stop_word and i not in stop_words and len(i) > 2 and not re.findall(\"\\W\",i) and not i.isdigit()]"
   ],
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   "execution_count": 11,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# 训练英文招聘文本word2vec模型\n",
    "print(\"开始训练英文招聘文本word2vec模型\")\n",
    "en_to_vec = EnToVec()\n",
    "en_to_vec.train(En_data)\n",
    "en_to_vec.model.save('../Data/word2vec model/en_to_vec.model')\n",
    "print(\"英文招聘文本word2vec模型已保存\")\n",
    "# 训练中文招聘文本word2vec模型"
   ],
   "metadata": {
    "execution": {
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    }
   },
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始训练英文招聘文本word2vec模型\n",
      "分词结束~\n",
      "训练模型中~\n",
      "英文招聘文本word2vec模型已保存\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始训练中文招聘文本word2vec模型\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\paddlenlp\\transformers\\tokenizer_utils_base.py:1865: UserWarning: Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分词结束~\n",
      "训练模型中~\n",
      "中文招聘文本word2vec模型已保存\n"
     ]
    }
   ],
   "source": [
    "print(\"开始训练中文招聘文本word2vec模型\")\n",
    "zh_to_vec = ZhToVec()\n",
    "zh_to_vec.train(Zh_data)\n",
    "zh_to_vec.model.save('../Data/word2vec model/zh_to_vec.model')\n",
    "print(\"中文招聘文本word2vec模型已保存\")"
   ],
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    "ExecuteTime": {
     "end_time": "2024-03-21T05:43:53.655002900Z",
     "start_time": "2024-03-21T05:40:23.001725100Z"
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1500x500 with 2 Axes>",
      "image/png": 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FORHJk271RNvtPOnWuHFjpk2bxrFjx1i7di1NmjTBYrHQuHFj1q5dS8mSJbHZbDd96u3fNGjQgPLlyzNs2DAiIiJuWdy7/uvjCy+8cMvFIv63yHgrDg4ON91v/GPBCxEREcm9nJ2dqVevHvXq1aNSpUo8+eSTLFiwgDFjxmTqPFnZZ7hVfys9PT3D66zs89yMzWbjwQcf5KWXXrrp8UqVKt32uYoWLUqHDh3o0KGDff6zEydO3PRps6zwv3OnZfVn1aFDB1xcXOjduzfJyck89thjN213/boffPABtWrVumkbDw+P27qm+qUit6binIjkCYcPH87wC+ORI0ew2WwEBAQA1yb1tdlsHD58OMOCCdHR0cTGxmboWF0vui1fvpwtW7YwcuRI4NriD1988QUlS5bE3d2doKCgTOfs3r07b731FlWrVr1lB+f6Y/xOTk60atXqX893u8Nq/42/vz8HDx68Yf+BAwfsx6//7549ezAMI8N1b/ZeERERufeuj0A4e/Zstpzf39+fXbt2YbPZMjwB9r99hutPUcXGxmZ4//8+HZZVfZ5bHStfvjwJCQn/ee7Mqlu3LqtXr+bs2bP4+/tTvnx59uzZ86/vud3P7lYy81ndjgIFChAaGsrs2bNp27atfQGH/3V9GKqnp2e290uLFSuGm5vbLfulVqsVPz8/4Nrndf2pvn9Sv1RyK805JyJ5wuTJkzO8njRpEoB9dbGHHnoI4IZVuT766CMA2rVrZ99XtmxZSpUqxccff0xqair3338/cK1od/ToURYuXEjDhg1xdMz87xv9+vVjzJgxTJgw4ZZtihcvTvPmzfnyyy9v2rk+f/68/Z/d3d2BGzu/mfHQQw+xefNmwsLC7PsSExOZOnUqAQEB9mExDz30EGfOnGHhwoX2dleuXGHq1Kl3fG0RERHJvJUrV970aaPr03Jk19C+hx56iKioKObPn2/fl5aWxqRJk/Dw8KBZs2bAtcKJg4PDDXO6ff755xleZ1Wfx93d/ab7H3vsMcLCwvj9999vOBYbG0taWtot7zUqKso+7+4/paSksGLFigzTpXTu3Nk+tPh/Xf+ebvezu5XMfFa364UXXmDMmDG3HCYLEBQURPny5fnwww9JSEj41+vebb/UwcGB1q1b8/PPP9unpoFrP6bPnTuXxo0b4+npCVz7PDdu3MjmzZszZJkzZ84dXVvEbHpyTkRyjN9++83+6+E/NWrUKMOksDcTERFBhw4daNOmDWFhYcyePZsePXoQGBgIQGBgIL1792bq1KnExsbSrFkzNm/ezMyZMwkNDaVFixYZztekSRPmzZtHzZo17b/+1qlTB3d3dw4dOpTp+eau8/f3Z+zYsf/ZbvLkyTRu3JiaNWvy9NNPU65cOaKjowkLC+PUqVPs3LkTgFq1auHg4MB7771HXFwcLi4uPPDAAxQvXvy2M40cOZLvvvuOtm3bMmTIEIoUKcLMmTOJiIjghx9+sP+6+/TTT/PZZ5/Rq1cvtm3bRokSJfj2229xc3O7o89CRERE7szgwYO5cuUKHTt2pEqVKqSkpLBhwwbmz59PQEAATz75ZLZct3///nz55Zf06dOHbdu2ERAQwMKFC1m/fj2ffPIJBQsWBMDLy4tHH32USZMmYbFYKF++PEuWLLnpHG9Z0ecJCgriiy++4K233qJChQoUL16cBx54gBdffJHFixfz8MMP06dPH4KCgkhMTGT37t0sXLiQ48eP3/KJsVOnTlG/fn0eeOABWrZsia+vL+fOneO7775j586dDBs2zP7eF198kYULF/Loo4/y1FNPERQUxMWLF1m8eDFTpkwhMDDwtj+7f3O7n9XtCgwMtPeVb8VqtfLVV1/Rtm1bqlevzpNPPkmpUqU4ffo0K1euxNPTk19++QXAPqrk1VdfpVu3bjg5OdG+fXt70e52vPXWWyxfvpzGjRvz3HPP4ejoyJdffklycnKGefFeeuklvv32W9q0acPQoUNxd3dn6tSp9icURXId09aJFRH52zfffGMAt9y++eYbe1vAGDNmjP319eXe9+3bZ3Tp0sUoWLCgUbhwYWPQoEHG1atXM1wnNTXVeOONN4yyZcsaTk5Ohp+fnzFq1KgMy9FfN3nyZAMwBgwYkGF/q1atDMBYsWLFbd2bv7+/0a5du9u6/y1btmTYf/ToUaNXr16Gr6+v4eTkZJQqVcp4+OGHjYULF2ZoN23aNKNcuXKGg4ODARgrV67812s3a9bMaNas2Q3X6tKli1GoUCHD1dXVqF+/vrFkyZIb3nvixAmjQ4cOhpubm+Ht7W0MHTrUWLZsWYbrioiISPb67bffjKeeesqoUqWK4eHhYTg7OxsVKlQwBg8ebERHR2do6+/vb/Tu3dv+euXKlTf8d7tZs2ZG9erVb7hO7969DX9//wz7oqOjjSeffNLw9vY2nJ2djZo1a2boq113/vx5o3Pnzoabm5tRuHBh45lnnjH27NlzQ9/OMO6+zxMVFWW0a9fOKFiwoAFk6OdcvnzZGDVqlFGhQgXD2dnZ8Pb2Nho1amR8+OGHRkpKyi0/4/j4eGPixIlGSEiIUbp0acPJyckoWLCgERwcbEybNs2w2WwZ2l+4cMEYNGiQUapUKcPZ2dkoXbq00bt3byMmJiZTn11ERIQBGB988MFNc93uZ3UzgDFw4MB/bXO9b33+/PkM+3fs2GF06tTJKFq0qOHi4mL4+/sbjz322A194nHjxhmlSpUyrFarARgRERH/eu3//ffTMAxj+/btRkhIiOHh4WG4ubkZLVq0MDZs2HDDe3ft2mU0a9bMcHV1NUqVKmWMGzfO+PrrrzNcVyS3sBiGZl8UERERERERERExg+acExERERERERERMYmKcyIiIiIiIiIiIiZRcU5ERERERERERMQkKs6JiIiIiIiIiIiYRMU5ERERERERERERk6g4JyIiIiIiIiIiYhJHswPkFTabjTNnzlCwYEEsFovZcURERCSXMAyDy5cvU7JkSaxW/W6aE6mfJyIiInfidvt5Ks5lkTNnzuDn52d2DBEREcmlTp48SenSpc2OITehfp6IiIjcjf/q56k4l0UKFiwIXPvAPT09TU4jIiIiuUV8fDx+fn72voTkPOrniYiIyJ243X6einNZ5PoQB09PT3XaREREJNM0XDLnUj9PRERE7sZ/9fM0sYmIiIiIiIiIiIhJVJwTERERERERERExiYpzIiIiIiIiIiIiJlFxTkRERERERERExCQqzomIiIiIiIiIiJhExTkRERERERERERGTqDgnIiIiIiIiIiJiEhXnRERERERERERETKLinIiIiIiIiIiIiElUnBMRERERERERETGJqcW5NWvW0L59e0qWLInFYmHRokUZjkdHR9OnTx9KliyJm5sbbdq04fDhwxnaJCUlMXDgQIoWLYqHhwedO3cmOjo6Q5vIyEjatWuHm5sbxYsX58UXXyQtLS1Dm1WrVlGnTh1cXFyoUKECM2bMyI5bFhERERERERERsTO1OJeYmEhgYCCTJ0++4ZhhGISGhnLs2DF+/vlnduzYgb+/P61atSIxMdHebvjw4fzyyy8sWLCA1atXc+bMGTp16mQ/np6eTrt27UhJSWHDhg3MnDmTGTNmMHr0aHubiIgI2rVrR4sWLQgPD2fYsGH069eP33//PXs/ABERERERERERydcshmEYZocAsFgs/PTTT4SGhgJw6NAhKleuzJ49e6hevToANpsNX19f3nnnHfr160dcXBzFihVj7ty5dOnSBYADBw5QtWpVwsLCaNiwIb/99hsPP/wwZ86cwcfHB4ApU6bw8ssvc/78eZydnXn55ZdZunQpe/bssefp1q0bsbGxLFu27Lbyx8fH4+XlRVxcHJ6enln4yfzjGkmpeLo6Zcu5RURExBz3og8hd+defEcJyWl4uDhmy7lFRETEHLfbh8ixc84lJycD4Orqat9ntVpxcXFh3bp1AGzbto3U1FRatWplb1OlShXKlClDWFgYAGFhYdSsWdNemAMICQkhPj6evXv32tv88xzX21w/x63yxcfHZ9iyS1q6jYl/Hqbp+ys5efFKtl1HRERERO4twzD4YtVRgt9Zwb4z2defFBERkZwrxxbnrhfZRo0axaVLl0hJSeG9997j1KlTnD17FoCoqCicnZ0pVKhQhvf6+PgQFRVlb/PPwtz149eP/Vub+Ph4rl69etN848ePx8vLy775+fnd9T3/m9WHzhF7JZUR34eTlm7L1muJiIiIyL1hsVjYcyaOy8lpjFm8hxwyqEVERETuoRxbnHNycuLHH3/k0KFDFClSBDc3N1auXEnbtm2xWs2PPWrUKOLi4uzbyZMns+1ajg5WJnarjYeLI1uOX+LzVUez7VoiIiIicm+9+lBVCjg5sOX4JX4OP2N2HBEREbnHzK9y/YugoCDCw8OJjY3l7NmzLFu2jAsXLlCuXDkAfH19SUlJITY2NsP7oqOj8fX1tbf539Vbr7/+rzaenp4UKFDgptlcXFzw9PTMsGUnvyJujAu9NvfexBWH2XbiUrZeT0RERETujZKFCjDogQoAvP3rfi4npZqcSERERO6lHF2cu87Ly4tixYpx+PBhtm7dyiOPPAJcK945OTmxYsUKe9uDBw8SGRlJcHAwAMHBwezevZtz587Z2yxfvhxPT0+qVatmb/PPc1xvc/0cOUVorVJ0CCxJus1g2Pwd6riJiIhInjR58mQCAgJwdXWlQYMGbN68+V/bL1iwgCpVquDq6krNmjX59ddfMxxPSEhg0KBBlC5dmgIFClCtWjWmTJmSnbeQaf2alCWgqBvnLycz6a8jZscRERGRe8jU4lxCQgLh4eGEh4cDEBERQXh4OJGRkcC1jtaqVas4duwYP//8Mw8++CChoaG0bt0auFa069u3LyNGjGDlypVs27aNJ598kuDgYBo2bAhA69atqVatGk888QQ7d+7k999/57XXXmPgwIG4uLgA8Oyzz3Ls2DFeeuklDhw4wOeff87333/P8OHD7/2H8i8sFgtvdaxBqUIFOHnxKmMW7zU7koiIiEiWmj9/PiNGjGDMmDFs376dwMBAQkJCMvzQ+k8bNmyge/fu9O3blx07dhAaGkpoaCh79uyxtxkxYgTLli1j9uzZ7N+/n2HDhjFo0CAWL158r27rP7k4OjCmw7VREtPXRXA4+rLJiUREROResRgmzjq7atUqWrRoccP+3r17M2PGDD799FM++OADoqOjKVGiBL169eL111/H2dnZ3jYpKYnnn3+e7777juTkZEJCQvj888/tQ1YBTpw4wYABA1i1ahXu7u707t2bd999F0fH/1+uftWqVQwfPpx9+/ZRunRpXn/9dfr06XPb93K7y+Nmha3HL/LYl2HYDPi0e206BJbM1uuJiIhI9rmXfYjcoEGDBtSrV4/PPvsMAJvNhp+fH4MHD2bkyJE3tO/atSuJiYksWbLEvq9hw4bUqlXL/nRcjRo16Nq1K6+//rq9TVBQEG3btuWtt9664ZzJyckkJyfbX8fHx+Pn53dPvqN+M7fy5/5o7q9QlNl9G2CxWLL1eiIiIpJ9brefZ2pxLi+51x3rj5Yf4tMVhyno6shvQ5tQurBbtl9TREREsp6Kc/8vJSUFNzc3Fi5cSGhoqH1/7969iY2N5eeff77hPWXKlGHEiBEMGzbMvm/MmDEsWrSInTt3AtC/f3927NjBokWLKFmyJKtWraJDhw4sXbqUpk2b3nDOsWPH8sYbb9yw/158R5EXrtDq49WkpNn4vGcdHqpZIluvJyIiItnndvt5uWLOObnRkAcqULtMIS4npTF8fjjpNtVYRUREJHeLiYkhPT0dHx+fDPt9fHyIioq66XuioqL+s/2kSZOoVq0apUuXxtnZmTZt2jB58uSbFuYARo0aRVxcnH07efLkXd7Z7StT1I1nm5UH4K0l+7iSknbPri0iIiLmUHEul3J0sDKxa208XBzZcvwSn6/UxMEiIiIiNzNp0iQ2btzI4sWL2bZtGxMmTGDgwIH8+eefN23v4uKCp6dnhu1eeq55eUoXLsCZuCQ+X3n0nl5bRERE7j0V53KxMkXdePORaxMHf7LiMNsjL5mcSEREROTOeXt74+DgQHR0dIb90dHRGeYT/idfX99/bX/16lVeeeUVPvroI9q3b899993HoEGD6Nq1Kx9++GH23MhdcnVy4PWHqwEwdc0xjsckmpxIREREspOKc7lcx9ql6BBYknSbwbB54VxOSjU7koiIiMgdcXZ2JigoiBUrVtj32Ww2VqxYQXBw8E3fExwcnKE9wPLly+3tU1NTSU1NxWrN2O11cHDAZrNl8R1kndbVfGhaqRgp6TbeXLLP7DgiIiKSjVScy+UsFgvjQmtQqlABIi9eYexidd5EREQk9xoxYgTTpk1j5syZ7N+/nwEDBpCYmMiTTz4JQK9evRg1apS9/dChQ1m2bBkTJkzgwIEDjB07lq1btzJo0CAAPD09adasGS+++CKrVq0iIiKCGTNmMGvWLDp27GjKPd4Oi8XCmPbVcHKw8NeBc6zYH/3fbxIREZFcScW5PMCrgBOfdKuF1QI/bD/FLzvPmB1JRERE5I5cH246evRoatWqRXh4OMuWLbMv+hAZGcnZs2ft7Rs1asTcuXOZOnUqgYGBLFy4kEWLFlGjRg17m3nz5lGvXj169uxJtWrVePfdd3n77bd59tln7/n9ZUb5Yh70bVwOgDd+2UdSarrJiURERCQ7WAzD0DKfWeB2l8fNTh/9cZBP/zpCQVdHfhvahNKF3UzJISIiIrcvJ/Qh5N+Z+R0lJqfxwIRVRMcn8/yDlRjcsuI9vb6IiIjcudvtQ+jJuTxkSMuK1C5TiMtJaYyYv5N0m+quIiIiIrmZu4sjr7a7tjjE5FVHOHXpismJREREJKupOJeHODpY+aRrLdydHdh8/CJfrDpidiQRERERuUvt7ytBg7JFSEq18fbS/WbHERERkSym4lwe41/UnTcfuTbHysd/HmZH5CWTE4mIiIjI3bBYLLzxSHUcrBZ+2xPF2sPnzY4kIiIiWUjFuTyoU51StA8sSbrNYOi8cBKS08yOJCIiIiJ3oYqvJ72C/QEYs3gvKWk2kxOJiIhIVlFxLg+yWCy8FVqDUoUKEHnxCmN+3mt2JBERERG5S8NaVcLbw5lj5xP5Zn2E2XFEREQki6g4l0d5FXDi4661sFrgh+2n+GXnGbMjiYiIiMhd8CrgxMi2VQH4dMVhouKSTE4kIiIiWUHFuTysftkiDGxRAYBXftrN6dirJicSERERkbvRqXYp6pQpRGJKOuN/0+IQIiIieYGKc3nckJYVqeVXiMtJaQyfF066zTA7koiIiIjcIavVwpuP1MBigZ/Dz7Dp2AWzI4mIiMhdUnEuj3NysDKxWy3cnR3YfPwiX6w6YnYkEREREbkLNUp50aN+GeDa4hBp6VocQkREJDdTcS4f8C/qzhuP1ADg4z8PE34y1txAIiIiInJXXmhdmUJuThyIuszsjSfMjiMiIiJ3QcW5fKJznVI8fF8J0m0GQ+ftICE5zexIIiIiInKHCrs782JIZQAmLD9ETEKyyYlERETkTqk4l09YLBbe7liTUoUKcOLCFcYu3mt2JBERERG5C93qlaFGKU8uJ6Xx/rIDZscRERGRO6TiXD7iVcCJj7vWwmqBhdtOsWTXGbMjiYiIiMgdcrBaeKPDtalLvt96ih2Rl0xOJCIiIndCxbl8pn7ZIjzXvAIAr/y4m9OxV01OJCIiIiJ3Ksi/MF2CSgMw+ue9pNsMkxOJiIhIZqk4lw8NbVWRWn6FiE9KY/j8cHXiRERERHKxl9tUoaCLI7tPxzF/y0mz44iIiEgmqTiXDzk5WJnYrRbuzg5sjrjIlNVHzY4kIiIiIneoWEEXhj9YCYD3fz/ApcQUkxOJiIhIZqg4l0/5F3XnjUeuzVHy8fJDhJ+MNTeQiIiIiNyxXsH+VPYpSOyVVCYsP2h2HBEREckEFefysc51StHuvhKk2QyGzttBYnKa2ZFERERE5A44Olh545HqAMzZFMme03EmJxIREZHbpeJcPmaxWHgntCYlvVw5ceEKYxfvNTuSiIiIiNyhhuWK0iGwJIYBo3/eg03zCouIiOQKKs7lc15uTnzctRZWCyzYdoqlu86aHUlERERE7tArD1XFzdmB7ZGx/LTjtNlxRERE5DaoOCc0KFeU55pXAGDUj7s4HXvV5EQiIiIicid8vVwZ0rIiAON/O0B8UqrJiUREROS/qDgnAAxtVZFAv0LEJ6UxYn446RoGISIiIpIrPXV/Wcp5uxOTkMzEPw+bHUdERET+g4pzAoCTg5WJXWvh7uzApoiLTFl91OxIIiIiInIHnB2tjO1wbXGIGRuOcyj6ssmJRERE5N+oOCd2Ad7u9o7cx8sPsfNkrLmBREREROSONK1UjJDqPqTbDMb8vBfD0KgIERGRnErFOcmgS1Bp2t1XgjSbwdB5O0hMTjM7koiIiIjcgdfaVcPF0UrYsQss3a1Fv0RERHIqFeckA4vFwjuhNSnp5crxC1d445e9ZkcSERERkTvgV8TNvujXW0v260dXERGRHErFObmBl5sTH3WthcUC3289xa/6pVVEREQkV3qmWTn8ihQgKj6Jz1YeMTuOiIiI3ISKc3JTDcsV5bnm5QEY+cMuzsReNTmRiIiIiGSWq5MDox++NqfwV2uPcex8gsmJRERE5H+pOCe3NKxVJQL9ChGflMbw+eGk2zSRsIiIiEhu06pqcZpXLkZqusHYX/ZpcQgREZEcRsU5uSUnBysTu9bCzdmBTREX+XLNUbMjiYiIiEgmWSwWxrSvjrODlTWHzrN8X7TZkUREROQfVJyTfxXg7c7YDteGQnz0xyF2now1N5CIiIiIZFpZb3eebloWgDeX7CMpNd3kRCIiInKdinPynx4NKk27miVIsxkMmx+ulb5EREREcqGBLSpQwsuVU5euMmW1RkSIiIjkFCrOyX+yWCy807EmJb1ciYhJ5I1f9podSUREREQyyc3ZkdfaVQPgi1VHOXnxismJREREBFSck9vk5ebER11rYbHA91tP8evus2ZHEhEREZFMeqimL43KFyU5zca4JfvMjiMiIiKYXJxbs2YN7du3p2TJklgsFhYtWpTheEJCAoMGDaJ06dIUKFCAatWqMWXKlAxtmjdvjsViybA9++yzGdpERkbSrl073NzcKF68OC+++CJpaRmHZq5atYo6derg4uJChQoVmDFjRnbccq7WsFxRBjQrD8DIH3ZxJvaqyYlEREREJDMsFgtvdKiOo9XCH/uiWXXwnNmRRERE8j1Ti3OJiYkEBgYyefLkmx4fMWIEy5YtY/bs2ezfv59hw4YxaNAgFi9enKHd008/zdmzZ+3b+++/bz+Wnp5Ou3btSElJYcOGDcycOZMZM2YwevRoe5uIiAjatWtHixYtCA8PZ9iwYfTr14/ff/89e248Fxv+YCUCS3sRn5TGiO/DSbcZZkcSERERkUyo6FOQPo0CAHjjl30kp2lxCBERETOZWpxr27Ytb731Fh07drzp8Q0bNtC7d2+aN29OQEAA/fv3JzAwkM2bN2do5+bmhq+vr33z9PS0H/vjjz/Yt28fs2fPplatWrRt25Zx48YxefJkUlJSAJgyZQply5ZlwoQJVK1alUGDBtGlSxc+/vjj7Lv5XMrJwcrEbrVxc3Zg47GLfLlGkwmLiIiI5DZDW1XE28OFiJhEvl4XYXYcERGRfC1HzznXqFEjFi9ezOnTpzEMg5UrV3Lo0CFat26dod2cOXPw9vamRo0ajBo1iitX/n9y27CwMGrWrImPj499X0hICPHx8ezdu9feplWrVhnOGRISQlhY2C2zJScnEx8fn2HLLwK83RnboToAH/1xiJ0nY80NJCIiIiKZUtDViVceqgLApBVHOBun6UpERETMkqOLc5MmTaJatWqULl0aZ2dn2rRpw+TJk2natKm9TY8ePZg9ezYrV65k1KhRfPvttzz++OP241FRURkKc4D9dVRU1L+2iY+P5+rVm3dUxo8fj5eXl33z8/PLknvOLR4NKs1DNX1JsxkMmx9OYnLaf79JRERERHKMjrVLUde/MFdT03l76X6z44iIiORbOb44t3HjRhYvXsy2bduYMGECAwcO5M8//7S36d+/PyEhIdSsWZOePXsya9YsfvrpJ44ezd7hlqNGjSIuLs6+nTx5Mluvl9NYLBbGd7yPEl6uRMQk8uYvWu1LREREJDexWCy88Uh1rBZYsussG47GmB1JREQkX8qxxbmrV6/yyiuv8NFHH9G+fXvuu+8+Bg0aRNeuXfnwww9v+b4GDRoAcOTIEQB8fX2Jjo7O0Ob6a19f339t4+npSYECBW56HRcXFzw9PTNs+Y2XmxMfd62FxQLzt57kt91nzY4kIiIiIplQvaQXjzf0B2Ds4r2kpttMTiQiIpL/5NjiXGpqKqmpqVitGSM6ODhgs9260xAeHg5AiRIlAAgODmb37t2cO/f/y8QvX74cT09PqlWrZm+zYsWKDOdZvnw5wcHBWXEreVrDckUZ0Kw8ACN/3M2ZWM1XIiIiIpKbjHiwEkXcnTkUncCssBNmxxEREcl3TC3OJSQkEB4ebi+oRUREEB4eTmRkJJ6enjRr1owXX3yRVatWERERwYwZM5g1a5Z9ddejR48ybtw4tm3bxvHjx1m8eDG9evWiadOm3HfffQC0bt2aatWq8cQTT7Bz505+//13XnvtNQYOHIiLiwsAzz77LMeOHeOll17iwIEDfP7553z//fcMHz7clM8ltxnWqhL3lfYi7moqI74PJ91mmB1JRERERG5TITdnXgqpDMAnyw9x7nKSyYlERETyF1OLc1u3bqV27drUrl0bgBEjRlC7dm1Gjx4NwLx586hXrx49e/akWrVqvPvuu7z99ts8++yzADg7O/Pnn3/SunVrqlSpwvPPP0/nzp355Zdf7NdwcHBgyZIlODg4EBwczOOPP06vXr1488037W3Kli3L0qVLWb58OYGBgUyYMIGvvvqKkJCQe/hp5F7OjlYmdquNm7MDG49dZOqaY2ZHEhEREZFMeKyuH4GlvbicnMZ7vx00O46IiEi+YjEMQ485ZYH4+Hi8vLyIi4vLl/PPAXy/5SQv/bALR6uFH59rxH2lC5kdSUREJMdTHyLnyy/fUfjJWEInrwfghwHBBPkXMTmRiIhI7na7fYgcO+ec5D6P1i3NQzV9SbMZDJ0XTmJymtmRREREJBeaPHkyAQEBuLq60qBBAzZv3vyv7RcsWECVKlVwdXWlZs2a/PrrrxmOWyyWm24ffPBBdt5GrlPLrxBd6/oBMPrnvZqqRERE5B5RcU6yjMVi4Z2ONSnh5UpETCLjluwzO5KIiIjkMvPnz2fEiBGMGTOG7du3ExgYSEhISIbFvf5pw4YNdO/enb59+7Jjxw5CQ0MJDQ1lz5499jZnz57NsE2fPh2LxULnzp3v1W3lGi+1qYynqyN7z8Tz3eZIs+OIiIjkCxrWmkXyy3CH2xF29AI9vtqIYcAXPevQtmYJsyOJiIjkWOpDZNSgQQPq1avHZ599BoDNZsPPz4/BgwczcuTIG9p37dqVxMRElixZYt/XsGFDatWqxZQpU256jdDQUC5fvsyKFStuK1N++45mbjjOmMV7KeTmxMrnm1PY3dnsSCIiIrmShrWKaYLLF+XZZuUBGPnjbs7GXTU5kYiIiOQGKSkpbNu2jVatWtn3Wa1WWrVqRVhY2E3fExYWlqE9QEhIyC3bR0dHs3TpUvr27XvLHMnJycTHx2fY8pOeDcpQxbcgsVdSef93LQ4hIiKS3VSck2wxvFUl7ivtRdzVVEbM36k5S0REROQ/xcTEkJ6ejo+PT4b9Pj4+REVF3fQ9UVFRmWo/c+ZMChYsSKdOnW6ZY/z48Xh5edk3Pz+/TN5J7uboYOXNR2oAMG9LJLtOxZobSEREJI9TcU6yhbOjlU+61qKAkwNhxy4wbe0xsyOJiIiIMH36dHr27Imrq+st24waNYq4uDj7dvLkyXuYMGeoX7YIobVKYhjXFoew6YdWERGRbKPinGSbcsU8GNuhGgAf/n6Q3afiTE4kIiIiOZm3tzcODg5ER0dn2B8dHY2vr+9N3+Pr63vb7deuXcvBgwfp16/fv+ZwcXHB09Mzw5YfjXqoKu7ODoSfjGXh9lNmxxEREcmzVJyTbPVYXT/a1vAlzWYwdN4OrqSkmR1JREREcihnZ2eCgoIyLNRgs9lYsWIFwcHBN31PcHDwDQs7LF++/Kbtv/76a4KCgggMDMza4HmUj6crw1pVAuC93w4QdzXV5EQiIiJ5k4pzkq0sFgvjO9XE19OVYzGJvPnLPrMjiYiISA42YsQIpk2bxsyZM9m/fz8DBgwgMTGRJ598EoBevXoxatQoe/uhQ4eybNkyJkyYwIEDBxg7dixbt25l0KBBGc4bHx/PggUL/vOpOcmoz/0BVCjuwYXEFD5efsjsOCIiInmSinOS7Qq5OfNR10AsFpi35STL9pw1O5KIiIjkUF27duXDDz9k9OjR1KpVi/DwcJYtW2Zf9CEyMpKzZ/+/L9GoUSPmzp3L1KlTCQwMZOHChSxatIgaNWpkOO+8efMwDIPu3bvf0/vJ7ZwcrIxtXx2AWWHH2X82f61cKyIici9YDMPQ7K5ZID4+Hi8vL+Li4vLtvCT/5d3fDjBl9VEKuTmxbGhTfL1uPRGziIhIfqE+RM6n7wiem7ONX3dHUb9sEeb3b4jFYjE7koiISI53u30IPTkn98yIBytRs5QXsVdSGfF9uFb9EhEREcklXm1XDVcnK5sjLrJ45xmz44iIiOQpKs7JPePsaGVit1oUcHJgw9ELTF17zOxIIiIiInIbShUqwKAWFQB459f9JCRrkS8REZGsouKc3FPlinkwpn01ACb8cZDdp+JMTiQiIiIit6Nfk3L4F3UjOj6ZSX8dNjuOiIhInqHinNxzXev50aa6L6npBkPn7eBKin55FREREcnpXJ0c7D+yfr02giPnEkxOJCIikjeoOCf3nMVi4d3ONfH1dOVYTCLjluwzO5KIiIiI3IYHqvjQskpx0mwGYxfvRWvLiYiI3D0V58QUhdyc+ahrIBYLfLf5JMv2RJkdSURERERuw+j21XB2sLLuSAy/71UfTkRE5G6pOCemaVTem/5NywEw8sddRMUlmZxIRERERP6Lf1F3nml2rQ83bsl+rqakm5xIREQkd1NxTkz1/IOVqVnKi9grqTy/IBybTUMjRERERHK655pXoFShApyOvcoXq46YHUdERCRXU3FOTOXsaOWTbrUo4OTA+iMXmLb2mNmRREREROQ/FHB24PWHqwIwZc0xTlxINDmRiIhI7qXinJiufDEP+8pfH/5xkD2n40xOJCIiIiL/JaS6L00qepOSZtMCXyIiIndBxTnJEbrW8yOkug+p6QZD5u3gSkqa2ZFERERE5F9YLBbGtK+Oo9XCn/vP8deBaLMjiYiI5EoqzkmOYLFYeLfTffh6unLsfCLjluw3O5KIiIiI/IcKxT3o27gsAG/8so+kVC0OISIiklkqzkmOUdjdmY8eC8Rige82R7JsT5TZkURERETkPwxuWZHiBV04ceEKX6+LMDuOiIhIrqPinOQojSp4079pOQBG/riLU5eumJxIRERERP6Nh4sjr7a7tjjEpL8Oczr2qsmJREREchcV5yTHef7BytQo5UnslVS6T9uoAp2IiIhIDtchsCT1A4qQlGrjnaWankRERCQzVJyTHMfZ0cq0XnUJKOrGyYtX6TZVBToRERGRnMxisfDGI9WxWmDp7rOsOxxjdiQREZFcQ8U5yZFKeBVgXv9gAoq6cerStQLdyYsq0ImIiIjkVFVLeNIrOACAMYv3kJJmMzeQiIhILqHinORYvl6uzOsfTFlvdxXoRERERHKB4Q9Woqi7M0fPJzJzw3Gz44iIiOQKKs5Jjubr5cp3TzekrLc7p2NVoBMRERHJybwKOPFymyoAfPLnIc7FJ5mcSEREJOdTcU5yvGtP0DWk3D8KdJEXVKATERERyYm6BJWmll8hElPSGf/bAbPjiIiI5Hgqzkmu4OPpynf9G1Ku2PUCXZgKdCIiIiI5kNVq4c1HqmOxwE87TrM54qLZkURERHI0Feck1/DxdGXe0w0pX8ydM3FJdJsaxokLiWbHEhEREZH/cV/pQnSrVwaA0T/vIS1di0OIiIjciopzkqsU//sJuv8v0G3keIwKdCIiIiI5zYshlfEq4MSBqMvM3RxpdhwREZEcS8U5yXWKF7xWoKtQ3IOzKtCJiIiI5EhF3J15IaQyAB/+fpALCckmJxIREcmZVJyTXKl4wWuruFYo7kFU/LUCXYQKdCIiIiI5So/6ZahWwpP4pDQ++P2g2XFERERyJBXnJNcqVtCF755uSEV7gS5MBToRERGRHMTh78UhAOZvPUn4yVhzA4mIiORAKs5JrlasoAvf9W9IJR8PouOT6TY1jGPnE8yOJSIiIiJ/qxtQhE51SmEYMObnPdhshtmRREREchQV5yTX8/ZwYe7TDansU/DvAt1GjqpAJyIiIpJjjGxbBQ8XR3aeiuP7rSfNjiMiIpKjqDgnecK1Al0DKvsU5NzlZLqrQCciIiKSYxQv6MqwVhUBeG/ZAWKvpJicSEREJOdQcU7yjKJ/F+iq+F4r0HWbupEj51SgExEREckJejcKoGJxDy5dSeWj5YfMjiMiIpJjmFqcW7NmDe3bt6dkyZJYLBYWLVqU4XhCQgKDBg2idOnSFChQgGrVqjFlypQMbZKSkhg4cCBFixbFw8ODzp07Ex0dnaFNZGQk7dq1w83NjeLFi/Piiy+SlpaWoc2qVauoU6cOLi4uVKhQgRkzZmTHLUs2K+rhwpx+1wp05y8n032aCnQiIiIiOYGTg5U3/l4cYvbGE3y78QSJyWn/8S4REZG8z9TiXGJiIoGBgUyePPmmx0eMGMGyZcuYPXs2+/fvZ9iwYQwaNIjFixfb2wwfPpxffvmFBQsWsHr1as6cOUOnTp3sx9PT02nXrh0pKSls2LCBmTNnMmPGDEaPHm1vExERQbt27WjRogXh4eEMGzaMfv368fvvv2ffzUu2Kfr3HHRVS3hy3v4E3WWzY4mIiIjke43Ke9MhsCQ2A15ftIcG76zg9UV7OBAVb3Y0ERER01gMw8gRyyVZLBZ++uknQkND7ftq1KhB165def311+37goKCaNu2LW+99RZxcXEUK1aMuXPn0qVLFwAOHDhA1apVCQsLo2HDhvz22288/PDDnDlzBh8fHwCmTJnCyy+/zPnz53F2dubll19m6dKl7Nmzx36dbt26ERsby7Jly24rf3x8PF5eXsTFxeHp6ZkFn4jcrUuJKfT4ahP7z8bj7eHMd083pKJPQbNjiYiIZKA+RM6n7yhrJaWm823YCeZujiQiJtG+P8i/MD0blOGhmiVwdXIwMaGIiEjWuN0+RI6ec65Ro0YsXryY06dPYxgGK1eu5NChQ7Ru3RqAbdu2kZqaSqtWrezvqVKlCmXKlCEsLAyAsLAwatasaS/MAYSEhBAfH8/evXvtbf55juttrp/jZpKTk4mPj8+wSc5S2N2Zuf0aUK2EJzEJKXSftpFD0XqCTkRERMRMrk4OPN20HCtGNGNOvwY8VNMXR6uFbScuMeL7nTQcv4K3l+7jmBb3EhGRfCJHF+cmTZpEtWrVKF26NM7OzrRp04bJkyfTtGlTAKKionB2dqZQoUIZ3ufj40NUVJS9zT8Lc9ePXz/2b23i4+O5evXqTbONHz8eLy8v++bn53fX9ytZr7C7M3P6NaB6yb8LdFNVoBMRERHJCaxWC/dX8ObznkFsGPkAL7SuRKlCBYi9ksq0tRE8MGE1Pb/ayK+7z5KabjM7roiISLbJ8cW5jRs3snjxYrZt28aECRMYOHAgf/75p9nRGDVqFHFxcfbt5MmTZkeSW7heoKtRypMLidcKdAejVKATERERySmKe7oy6IGKrHmpBdP71KVlleJYLLD+yAWem7OdRu/+xYe/H+R07M1/OBcREcnNHM0OcCtXr17llVde4aeffqJdu3YA3HfffYSHh/Phhx/SqlUrfH19SUlJITY2NsPTc9HR0fj6+gLg6+vL5s2bM5z7+mqu/2zzvyu8RkdH4+npSYECBW6az8XFBRcXlyy5V8l+hdycmd23AY9/vYk9p+PpMW0jc55uQBVfzRsjIiIiklM4WC08UMWHB6r4cOrSFeZtPsm8LSc5fzmZz1Ye4fNVR2hRuTg9G5ahWaXiOFgtZkcWERG5azn2ybnU1FRSU1OxWjNGdHBwwGa79lh7UFAQTk5OrFixwn784MGDREZGEhwcDEBwcDC7d+/m3Llz9jbLly/H09OTatWq2dv88xzX21w/h+QNhdycmdO3ITVLeXEhMYUe064tFiEiIiIiOU/pwm68EFKZsFEP8HnPOtxfoSg2A1YcOMdTM7bS9P2VfPbXYc5dTjI7qoiIyF0xdbXWhIQEjhw5AkDt2rX56KOPaNGiBUWKFKFMmTI0b96cmJgYPvvsM/z9/Vm9ejUDBgzgo48+YsCAAQAMGDCAX3/9lRkzZuDp6cngwYMB2LBhAwDp6enUqlWLkiVL8v777xMVFcUTTzxBv379eOeddwCIiIigRo0aDBw4kKeeeoq//vqLIUOGsHTpUkJCQm7rXrSKV+4RdyWVJ6ZvYtepOAq7OTH36YZULaHvTEREzKE+RM6n7yjnOHY+ge82R7Jg2ylir6QC4Gi10Lq6Dz0b+BNcrihWPU0nIiI5xO32IUwtzq1atYoWLVrcsL93797MmDGDqKgoRo0axR9//MHFixfx9/enf//+DB8+HIvl2n90k5KSeP755/nuu+9ITk4mJCSEzz//3D5kFeDEiRMMGDCAVatW4e7uTu/evXn33XdxdHTMkGX48OHs27eP0qVL8/rrr9OnT5/bvhd12nKXuKup9Pp6Ezv/LtDN6deQaiX1vYmIyL2nPkTOp+8o50lKTee3PWeZvTGSbScu2feX9XanR/0ydAkqTWF3ZxMTioiI5JLiXF6iTlvuE3c1lV7TN7PzZKwKdCIiYhr1IXI+fUc52/6z8czdFMlPO06TkJwGgLOjlYdrlqBnwzLUKVPY/sO+iIjIvaTi3D2mTlvuFJ+UyhNfXyvQFXJzYk6/BlQv6WV2LBERyUfUh8j59B3lDonJaSzeeYbZG0+w98z/zytcxbcgPRuUIbR2KQq6OpmYUERE8hsV5+4xddpyr/ikVHp9vZnwk7F4FbhWoKtRSgU6ERG5N9SHyPn0HeUuhmGw61QcczadYPHOMySlXltMzs3ZgUdqlaRnA3/19URE5J5Qce4eU6ctd4tPSqX39M3siFSBTkRE7i31IXI+fUe5V9zVVH7aforZmyI5ci7Bvj/QrxA9G5Sh/X0lKeDsYGJCERHJy1Scu8fUacv9Lv9doNuuAp2IiNxD6kPkfPqOcj/DMNgccZE5myL5bc9ZUtOv/RWooKsjneuUpmeDMlT0KWhyShERyWtutw9hvYeZRHK0gq5OzHyqPkH+hYm7mkqPaRvZfSrO7FgiIiL5zuTJkwkICMDV1ZUGDRqwefPmf22/YMECqlSpgqurKzVr1uTXX3+9oc3+/fvp0KEDXl5euLu7U69ePSIjI7PrFiSHsVgsNChXlE+71yZsVEtGtq1CmSJuXE5KY8aG4zz48Roe+zKMn8NPk5yWbnZcERHJZ1ScE/mHfxbo4pPS6PnVRnadijU7loiISL4xf/58RowYwZgxY9i+fTuBgYGEhIRw7ty5m7bfsGED3bt3p2/fvuzYsYPQ0FBCQ0PZs2ePvc3Ro0dp3LgxVapUYdWqVezatYvXX38dV1fXe3VbkoN4e7jwbLPyrHqhObOeqk/raj44WC1sjrjI0HnhBI//i/G/7SfywhWzo4qISD6hYa1ZRMMd8paE5DT6TN/M1hOXKOjqyOy+DQj0K2R2LBERyYPUh8ioQYMG1KtXj88++wwAm82Gn58fgwcPZuTIkTe079q1K4mJiSxZssS+r2HDhtSqVYspU6YA0K1bN5ycnPj222/vKJO+o7zvbNxV5m85ybzNJ4mKT7Lvb1qpGD0blKFlleI4Oui5BhERyRwNaxW5Cx4ujsx4qj71AgpzOSmNx7/eRPjJWLNjiYiI5GkpKSls27aNVq1a2fdZrVZatWpFWFjYTd8TFhaWoT1ASEiIvb3NZmPp0qVUqlSJkJAQihcvToMGDVi0aNEtcyQnJxMfH59hk7ythFcBhrWqxLqXWzD1iSCaVSqGxQJrDp3nmW+30fi9lXy8/BBn466aHVVERPIgFedEbsHDxZFvnqxP/YAiXE5K44mvVKATERHJTjExMaSnp+Pj45Nhv4+PD1FRUTd9T1RU1L+2P3fuHAkJCbz77ru0adOGP/74g44dO9KpUydWr15903OOHz8eLy8v++bn55cFdye5gaODldbVfZn5VH1Wv9CCAc3LU9Tdmaj4JCauOMz97/7F07O2svrQeWw2DUASEZGsoeKcyL+4VqCrR/2yRbicfK1AtyPyktmxREREcqSkpKT/bnSP2Ww2AB555BGGDx9OrVq1GDlyJA8//LB92Ov/GjVqFHFxcfbt5MmT9zKy5BBlirrxcpsqbBj1AJ92r02DskWwGbB8XzS9p2+m2Ycr+WLVUWISks2OKnJPJKWmM3XNUR6etJZle27+g4mI3BkV50T+g7uLI9/0+UeB7uvNbFeBTkREBLhW/Bo3bhylSpXCw8ODY8eOAfD666/z9ddfZ+pc3t7eODg4EB0dnWF/dHQ0vr6+N32Pr6/vv7b39vbG0dGRatWqZWhTtWrVW67W6uLigqenZ4ZN8i8XRwc6BJZk/jPB/DmiKU/eH4CnqyMnL17lvWUHCB6/gsHf7WDjsQtoOm/Ji9LSbczfEkmLD1fxzq8H2HM6nk9XHDY7lkieouKcyG1wd3FkxpP1aFC2CAnJafT6ejPbTqhAJyIi8tZbbzFjxgzef/99nJ2d7ftr1KjBV199lalzOTs7ExQUxIoVK+z7bDYbK1asIDg4+KbvCQ4OztAeYPny5fb2zs7O1KtXj4MHD2Zoc+jQIfz9/TOVT6RC8YKMaV+dTa+04oMu91HLrxCp6Qa/7DxDt6kbafXRaqaviyDuSqrZUUXummEYLNsTRZuJa3n5h92cjUuihJcrFgvsOxtPVFzOe1paJLdScU7kNrk5Xxvi2rDctQJd7+mb2XbiotmxRERETDVr1iymTp1Kz549cXBwsO8PDAzkwIEDmT7fiBEjmDZtGjNnzmT//v0MGDCAxMREnnzySQB69erFqFGj7O2HDh3KsmXLmDBhAgcOHGDs2LFs3bqVQYMG2du8+OKLzJ8/n2nTpnHkyBE+++wzfvnlF5577rm7uHPJzwo4O/BoXT8WDbyfJYMb071+GdycHTh6PpE3l+yj/jt/8sKCnYSfjNXTdJIrbTx2gU5fbODZ2ds4ci6BQm5OvPpQVVa+0JzA0oUAWH3onLkhRfIQR7MDiOQmbs6OTO9Tj74zthJ27AK9vt7MrL71CfIvYnY0ERERU5w+fZoKFSrcsN9ms5Gamvmnh7p27cr58+cZPXo0UVFR1KpVi2XLltkXfYiMjMRq/f/flxs1asTcuXN57bXXeOWVV6hYsSKLFi2iRo0a9jYdO3ZkypQpjB8/niFDhlC5cmV++OEHGjdufAd3LJJRjVJejO9Uk1ceqsKi8DPM2XiCA1GXWbjtFAu3naJ6SU96NvDnsbqlcXTQsxGSs+09E8f7yw6y+tB5AAo4OdC3cVn6NyuHp6sTAC0qFyf8ZCwrD5yna70yZsYVyTMshn7KyRLx8fF4eXkRFxeneUnygasp6fSduYUNRy/g7uzAzKfqUzdABToREcm83N6HCAoKYvjw4Tz++OMULFiQnTt3Uq5cOd58802WL1/O2rVrzY5413L7dyT3lmEYbI+MZc6mEyzZdZaUtL8XJalVkk+61sJisZicUORGkReuMGH5QX4OPwOAo9VCt/p+DHmgIsU9XTO03XUqlg6frcfDxZHtrz+Is6OKziK3crt9CD05J3IHCjg78HXvevSbtYX1Ry7Qa/pmZj5Vn3oq0ImISD4zevRoevfuzenTp7HZbPz4448cPHiQWbNmsWTJErPjidxzFouFIP/CBPkX5vV21fh+60k++P1a0aNqCU+ebVbe7IgiducvJ/PZX4eZuzmS1PRrz+20DyzJ8w9WIsDb/abvqVHSC28PZ2ISUth64iKNynvfy8gieZJK3CJ3qICzA1/1qkfjCt5cSUmn9/TNbI7QHHQiIpK/PPLII/zyyy/8+eefuLu7M3r0aPbv388vv/zCgw8+aHY8EVMVdnfmmWblGdP+2mrB7y07wF8Hov/jXSLZ73JSKh/9cZBmH6xkZtgJUtMNmlT0ZsngxkzqXvuWhTkAq9VCs0rFAVh18Py9iiySp6k4J3IXCjg78FXvuvYCXZ9vNrPp2AWzY4mIiNwTaWlpvPnmm5QtW5bly5dz7tw5rly5wrp162jdurXZ8URyjMcb+tO9fhkMA4Z+F86Rc5fNjiT5VHJaOl+vi6DZB6v49K8jXElJJ7C0F3P7NeDbvg2oUcrrts7TokoxAP46oEUhRLKCinMid8nV6VqBrknFawW6J2dsUYFORETyBUdHR95//33S0tLMjiKSo1ksFt7oUJ36AUW4nJzG07O2EXcl8wumiNypdJvBD9tO8cCHqxm3ZB8XE1Mo5+3OFz3rsGjg/TSqkLmhqU0qFMPBauHIuQROXrySTalF8g8V50SygKuTA9N61aVppWJ/P0G3hY0q0ImISD7QsmVLVq9ebXYMkRzP2dHK54/XoVShAkTEJDJ43g7S0m1mx5I8zjAM/twXzUMT1/L8gp2cjr2Kj6cL4zvV5I/hTWlbs8QdLVLi5eZEUJnCAKw6qKfnRO6WFoQQySKuTg5MfSKI/t9uY82h8zz5zRam96lHcPmiZkcTERHJNm3btmXkyJHs3r2boKAg3N0zzlPUoUMHk5KJ5DzeHi5M7RVEly/CWHPoPO/+doDXHq5mdizJo7Yev8i7vx1g64lLAHi6OjKgeQX6NAqggLPDXZ+/eZVibD5+kZUHz/NEcMBdn08kP7MYhmGYHSIvuN3lcSXvS0pN55lvt7H60HlcnaxM71NPKxiJiMgt5fY+hNV664EYFouF9PT0e5gme+T270hynqW7zjJw7nYAJjwaSOeg0iYnkrzkYNRlPvj9AH/uv/ZEm4ujlSfvL8uAZuXxcnPKsuvsPxtP24lrcXWyEj66Na5Od1/wE8lrbrcPoWGtIlnM1cmBL58IonnlYiSl2nhqxhY2HIkxO5aIiEi2sNlst9zyQmFOJDu0u68EQx6oAMCon3azI/KSyYkkLzh16Qojvg+nzcQ1/Ln/HA5WC93r+7H6xRaMbFslSwtzAFV8C1LCy5WkVJum9BG5SyrOiWQDVycHpjweRIvrBbqZW1ivAp2IiIiI/G1Yq0q0ruZDSpqNZ77dRlRcktmRJJe6mJjCm7/s44EPV/Pj9tMYBjxU05c/hjdlfKf78PVyzZbrWiwWmlcuDsCqg+ez5Roi+YWKcyLZxNXJgSlPBPFAleL2J+jWHVaBTkRE8p7Vq1fTvn17KlSoQIUKFejQoQNr1641O5ZIjma1Wvioay0q+Xhw7nIyz3y7laRUPW0qty8xOY1PVxym6fsrmb4+gpR0G43KF+Xngffzec8gyhfzyPYMLSoXA+CvA+fQjFkid07FOZFs5OLowBeP16FlleIkp9noO3MLaw/rVyUREck7Zs+eTatWrXBzc2PIkCEMGTKEAgUK0LJlS+bOnWt2PJEczcPFka961aOQmxM7T8Ux6sfdKnDIf0pJszFzw3GafbCSj5YfIiE5jeolPZn1VH3m9GtAoF+he5bl/greODlYiLx4hYiYxHt2XZG8RgtCZBFNFCz/JjktnYFztvPn/nO4OFqZ1qsuTSsVMzuWiIjkALm9D1G1alX69+/P8OHDM+z/6KOPmDZtGvv37zcpWdbJ7d+R5HwbjsTwxPTNpNsMXnmoCv2bljc7kuRANpvBL7vOMOGPQ0RevAKAf1E3nm9dmYdrlsBqtZiS6/GvNrHuSAyvP1yNvo3LmpJBJKfSghAiOYiLowOTe9ahVdVrT9D1m7WVNYf0BJ2IiOR+x44do3379jfs79ChAxERESYkEsl9GlXwZvTD1QB497cDrDx4zuREkpMYhsHKg+doN2kdQ+eFE3nxCt4eLowLrcGfI5rRIbCkaYU5gOZ/D21dpX9vRe6YinMi94iLowOf9wziwb8n/u03ayurVaATEZFczs/PjxUrVtyw/88//8TPz8+ERCK5U69gf7rV88NmwJDvdnD0fILZkSQH2B55iW5TN/LkN1vYfzaegi6OvNC6Emteas4TDf1xcjD/r/QtqlxbFGLTsYskJqeZnEYkd3I0O4BIfuLsaGVyjzoMmrudP/ZF8/SsrUx9Isi+ypGIiEhu8/zzzzNkyBDCw8Np1KgRAOvXr2fGjBlMnDjR5HQiuYfFYuHNR2pw5FwCW09c4umZW/lp4P14FXAyO5qY4Mi5y3zw+0F+3xsNXPt7RO9gf55rXoHC7s4mp8uonLc7ZYq4EXnxCuuPxNC6uq/ZkURyHfPL7CL5jLOjlc961CGk+rUn6PrP2qahCyIikmsNGDCAefPmsXv3boYNG8awYcPYs2cP8+fP55lnnjE7nkiu4uxo5YvHgyjp5cqxmESGfLeDdJumCM9PzsRe5eWFu2j98Rp+3xuN1QKPBpVm5QvNebVdtRxXmINrheXrq7auPKiRQSJ3QgtCZBFNFCyZlZpuY9Dc7fy+NxpnBytfPhFkfyRcRETyD/Uhcj59R3Kv7TkdR5cpG0hKtfFM03KMeqiq2ZEkm8VeSeHzVUeZseE4KWk2AB6s5sOLIZWp5FPQ5HT/beXBczz5zRZKeLmyYeQDWCzmzYEnkpNoQQiRHM7J4doTdG2q+5KSbuOZb7fxc/hps2OJiIhkypYtW9i0adMN+zdt2sTWrVtNSCSS+9Uo5cUHXQIB+HLNMX7cfsrkRJJdrqakM3nlEZq8v5Kpa46RkmajfkARfhgQzLRedXNFYQ4guFxRXJ2snI1L4mD0ZbPjiOQ6Ks6JmMjJwcqkHrV5qOa1At3QeeG8tWQfaek2s6OJiIjcloEDB3Ly5Mkb9p8+fZqBAweakEgkb2gfWJKBLcoDMPLH3YSfjDU3kGSp1HQbszeeoNkHK/ng94NcTkqjim9BvulTj/nPNCTIv4jZETPF1cmBRuW9AVh5QENbRTJLxTkRkzk5WJnUvY698/XVugie+HozFxKSTU4mIiLy3/bt20edOnVu2F+7dm327dtnQiKRvOP5ByvTqmpxUtJsPPPtVs7FJ5kdSe6SzWawZNcZWn+8htcW7eHc5WRKFy7Ax10D+XVIE1pUKZ5rh4T+/7xzmk9bJLNUnBPJARysFl4MqcKUx+vg7uxA2LELtJ+0jt2n4syOJiIi8q9cXFyIjo6+Yf/Zs2dxdHQ0IZFI3mG1Wvi4ay0qFvcgOj6Z/t9uIyk13exYcofWHY7hkcnrGTR3BxExiRR1d2Zs+2qseL4ZHWuXxmrNnUW565pXvjZ/9rYTl4i7mmpyGpHcRcU5kRykTY0SLBp4P2W93TkTl0TnKRtYsPXGoUIiIiI5RevWrRk1ahRxcf//g1JsbCyvvPIKDz74oInJRPKGgq5OTOtVF68CToSfjOXVn/agNf1yl12nYnn8q008/vUmdp+Ow93ZgWGtKrL6pRb0ub8sLo4OZkfMEn5F3KhQ3IN0m8G6wzFmxxHJVVScE8lhKvoU5OdB99uHMLy4cBejf95jX7VJREQkJ/nwww85efIk/v7+tGjRghYtWlC2bFmioqKYMGGC2fFE8oQAb3cm96iDg9XCD9tP8fW6CLMjyW04dj6BgXO20+Gz9aw7EoOTg4Un7w9gzUstGNaqEh4uee/pYg1tFbkzKs6J5ECerk5MfaIuw1pVBGBW2Al6frWRc5c1z4iIiOQspUqVYteuXbz//vtUq1aNoKAgJk6cyO7du/Hz8zM7nkie0biiN68+VBWAd37dz+pDmnQ/p4qOT+KVn3bz4MdrWLr7LBYLdKpdir+eb86Y9tUp6uFidsRs0+Lvoa2rDp7HZtMTniK3y2LomegsER8fj5eXF3FxcXh6epodR/KQ5fuiGTE/nMvJafh4uvDF40HUKVPY7FgiIpJF1IfI+fQdSU5hGAYvLdzFgm2n8HR15OdBjSnr7W52LPlb3NVUpqw+yjfrI0hKvTbqpWWV4rwQUpmqJfLHnx0paTZqv/kHiSnpLB50P/eVLmR2JBFT3W4fQk/OieRwD1bzYdGg+6nw90TA3b7cyHebI82OJSIi+dyhQ4fYvHlzhn0rVqygRYsW1K9fn3feecekZCJ5l8Vi4a2ONahTphDxSWn0m7mF+CRNvG+2pNR0vlx9lKbvr+SLVUdJSrUR5F+Y758J5us+9fJNYQ7A2dFK44reAKw8oKc7RW6XqcW5NWvW0L59e0qWLInFYmHRokUZjlsslptuH3zwgb1NQEDADcfffffdDOfZtWsXTZo0wdXVFT8/P95///0bsixYsIAqVarg6upKzZo1+fXXX7PlnkXuRPliHiwaeD9tqvuSkm5j1I+7GfXjbpLTtFqXiIiY4+WXX2bJkiX21xEREbRv3x5nZ2eCg4MZP348n3zyiXkBRfIoF0cHpjwRRAkvV46eT2TodztI1/BB06w+dJ4WH65i/G8HiLuaSiUfD6b1qsvCZ4OpX7aI2fFMcX1oq+adE7l9phbnEhMTCQwMZPLkyTc9fvbs2Qzb9OnTsVgsdO7cOUO7N998M0O7wYMH24/Fx8fTunVr/P392bZtGx988AFjx45l6tSp9jYbNmyge/fu9O3blx07dhAaGkpoaCh79uzJnhsXuQMeLo588XgdXgypjMUC322OpNvUjUTFaR46ERG597Zu3Urbtm3tr+fMmUOlSpX4/fffmThxIp988gkzZswwL6BIHla8oCtTn6iLi6OVlQfP88HvB82OlO+k2ww+Wn6IPt9s5mxcEiW9XPmgy338NrQpD1bzwWKxmB3RNM3/Ls7tPBXLhYRkk9OI5A6mFufatm3LW2+9RceOHW963NfXN8P2888/06JFC8qVK5ehXcGCBTO0c3f//3kX5syZQ0pKCtOnT6d69ep069aNIUOG8NFHH9nbTJw4kTZt2vDiiy9StWpVxo0bR506dfjss8+y58ZF7pDFYmFgiwp806cenq6O7IiM5eFJ69hy/KLZ0UREJJ+JiYmhdOnS9tcrV66kffv29tfNmzfn+PHjJiQTyR9qlvbi/S73ATBl9VF+Dj9tcqL8IyYhmd7TN/PpisMYBvRsUIa/XmjOo3X9cLDm36Lcdb5erlQt4YlhwJrDGtoqcjtyzZxz0dHRLF26lL59+95w7N1336Vo0aLUrl2bDz74gLS0NPuxsLAwmjZtirOzs31fSEgIBw8e5NKlS/Y2rVq1ynDOkJAQwsLCbpknOTmZ+Pj4DJvIvdK8cnF+GdyYKr4FiUlIpvvUjcwKO47WdxERkXulSJEinD17FgCbzcbWrVtp2LCh/XhKSor+uySSzR6pVYpnm5UH4KWFu9h1KtbcQPnA1uMXefjTdaw7EkMBJwc+7hrI2x1r4urkYHa0HKVF5WKA5p0TuV25pjg3c+ZMChYsSKdOnTLsHzJkCPPmzWPlypU888wzvPPOO7z00kv241FRUfj4+GR4z/XXUVFR/9rm+vGbGT9+PF5eXvbNz8/vru5PJLP8i7rz43ONaB9YkjSbweif9/LCgl0kpWoeOhERyX7Nmzdn3LhxnDx5kk8++QSbzUbz5s3tx/ft20dAQIBp+UTyixdDKvNAleIkp9noP2sb5+I15Ul2MAyDr9YeuzatTHwS5Yu58/Og++lYu/R/vzkfeqDKtaGtqw+d15yIIrch1xTnpk+fTs+ePXF1dc2wf8SIETRv3pz77ruPZ599lgkTJjBp0iSSk7N3bPuoUaOIi4uzbydPnszW64ncjJuzI592q8WrD1XFaoEftp/i0SlhnI69anY0ERHJ495++20OHDiAv78/L7/8Mu+//36GqUW+/fZbHnjgARMTiuQPDlYLn3SrRfli7kTFJ/Hs7G1aNCyLxSel8uzsbby1dD9pNoP2gSVZPKgxlXwKmh0tx6rlVwivAk7EXU0l/OQls+OI5Hi5oji3du1aDh48SL9+/f6zbYMGDUhLS7PPceLr60t0dHSGNtdf+/r6/mub68dvxsXFBU9PzwybiBksFgtPNy3Ht30bUNjNid2n42g/aR0bjsaYHU1ERPKwgIAA9u/fz44dOzhx4gQDBgzIcPyNN97gtddeMymdSP7i6erEV72vzUm8PTKW137ao2HlWWTvmTg6TFrH73ujcXKw8OYj1fm0Wy3cXRzNjpajOTpYaVpJQ1tFbleuKM59/fXXBAUFERgY+J9tw8PDsVqtFC9+7THa4OBg1qxZQ2pqqr3N8uXLqVy5MoULF7a3WbFiRYbzLF++nODg4Cy8C5HsdX8Fb34Z3JjqJT25mJjCE19v5qu1x9QxExGRbOPo6EhgYCAlS5a84VhgYCBFixY1IZVI/lTW253PetTBaoEF207xzfrjZkfK9b7fcpJOn2/g+IUrlCpUgAXPNqJXcEC+Xok1M+zzzh08Z3ISkZzP1OJcQkIC4eHhhIeHAxAREUF4eDiRkZH2NvHx8SxYsOCmT82FhYXxySefsHPnTo4dO8acOXMYPnw4jz/+uL3w1qNHD5ydnenbty979+5l/vz5TJw4kREjRtjPM3ToUJYtW8aECRM4cOAAY8eOZevWrQwaNCh7PwCRLFa6sBs/DGhEp9qlSLcZvLV0P8Pmh3M1RUMbRERERPK6ppWK8cpDVQF4a+k+1mqlzDtyNSWdFxfs5KUfdpGcZqNF5WIsGdyYWn6FzI6WqzStVAyLBfaeiSdacyGK/CtTi3Nbt26ldu3a1K5dG7g2f1zt2rUZPXq0vc28efMwDIPu3bvf8H4XFxfmzZtHs2bNqF69Om+//TbDhw9n6tSp9jZeXl788ccfREREEBQUxPPPP8/o0aPp37+/vU2jRo2YO3cuU6dOJTAwkIULF7Jo0SJq1KiRjXcvkj1cnRyY8FggY9tXw8Fq4efwM3T6YgMnL14xO5qIiIiIZLO+jcvSuU5pbAYMmruD4zGJZkfKVSJiEun4+XoWbDuF1XJtwY2ve9ejsLuz2dFyHW8PF+4rXQiAVXp6TuRfWQyNecsS8fHxeHl5ERcXp/nnJMfYeOwCg+ZuJyYhhUJuTnzarbZ97gcREckZ1IfI+fQdSW6TlJpOt6kbCT8ZS4XiHvz0XCMKujqZHSvH+233WV5cuIuE5DS8PZz5tFttGlXwNjtWrvbJn4f45M/DtKnuy5QngsyOI3LP3W4fIlfMOScid6ZhuaL8MrgxgX6FiL2SSp9vNvPFqqOah05EREQkD3N1cmDqE0H4eLpw5FwCw+aFk25T/+9WUtNtjFuyjwFztpOQnEa9gMIsHdJEhbks0KLytbng1x2JISXNZnIakZxLxTmRPK6EVwHm929I17p+2Ax4b9kBBs7dTmJymtnRREQkj1i7di2PP/44wcHBnD59GoBvv/2WdevWmZxMJP8q7unK1Cfq4uxoZcWBc0z446DZkXKks3FX6TZ1I1+viwDgmablmPt0Q3w8XU1OljfULOVFUXdnEpLT2HriotlxRHKsTBXnNm/eTHr6rSeWT05O5vvvv7/rUCKStVydHHi3c03e7lgDJwcLv+6OouPn64nQHCQiInKXfvjhB0JCQihQoAA7duwgOTkZgLi4ON555x2T04nkb4F+hXivc00APl91lMU7z5icKGdZe/g87T5dx7YTlyjo6siXTwQx6qGqODnoGZasYrVaaPb3qq2rDmqBEpFbydSfOsHBwVy4cMH+2tPTk2PHjtlfx8bG3nThBhExn8VioWcDf+b1D6Z4QRcORSfQ4bN1/HUg2uxoIiKSi7311ltMmTKFadOm4eT0/3Na3X///Wzfvt3EZCIC0LF2aZ5pWg6AlxbuZM/pOJMTmc9mM5j452F6Td/MxcQUqpXwZMngxoRU9zU7Wp50fWjrygNaFELkVjJVnPvfeapuNm+V5rISydmC/AuzZHBjgvwLczkpjb4ztzLxz8PYNA+JiIjcgYMHD9K0adMb9nt5eREbG3vvA4nIDV5qU4XmlYuRlGrj6VlbOX852exIprmYmEKfGVv4+M9DGAZ0r+/Hj881wr+ou9nR8qymFYvhYLVw+FwCJy9eMTuOSI6U5c/rWiyWrD6liGSx4p6ufPd0Q55o6I9hwMd/HuKZ2du4nJRqdjQREcllfH19OXLkyA37161bR7ly5UxIJCL/y8FqYWK32pQr5s7ZuCSenb2N5LRbT1eUV22PvES7T9ey5tB5XJ2sfPhoIOM73Yerk4PZ0fI0LzcngsoUBmDVIQ1tFbkZDaYXyaecHa2MC63B+53vw9nByvJ90TwyeT1HziWYHU1ERHKRp59+mqFDh7Jp0yYsFgtnzpxhzpw5vPDCCwwYMMDseCLyN68CTkzrVZeCro5sO3GJ0Yv25ptRT4Zh8M36CB6bEsbZuCTKeruzaOD9dAkqbXa0fKN5lb/nndPQVpGbcszsG/bt20dUVBRw7Q+5AwcOkJBw7S/zMTExWZtORLLdY/X8qORbkAGzt3HsfCKhk9cz4bFAzbkhIiK3ZeTIkdhsNlq2bMmVK1do2rQpLi4uvPDCCwwePNjseCLyD+WLeTCpe22emrGF+VtPUrVEQfrcX9bsWNnqclIqI3/YzdLdZwFoV7ME73auSUFXp/94p2SlFpWL8/6yg6w/GkNSarqeVhT5HxYjEz+XWK1WLBbLTX9hub7fYrH864queVV8fDxeXl7ExcXh6elpdhyRTItJSGbgnO1siri2xPngByowrFUlHKwaqi4ikp3ySh8iJSWFI0eOkJCQQLVq1fDw8DA7UpbJK9+RyHVT1xzlnV8P4GC1MOup+txfwdvsSNniQFQ8z83ezrGYRBytFl5tV5U+jQI0FZMJDMMgePxfRMUnMfOp+jSrVMzsSCL3xO32ITI1rDUiIoJjx44RERFxw3Z9/z9XbxWR3MPbw4XZ/Rrw5P0BAEz66wh9Z24h7ormoRMRkVt76qmnuHz5Ms7OzlSrVo369evj4eFBYmIiTz311B2dc/LkyQQEBODq6kqDBg3YvHnzv7ZfsGABVapUwdXVlZo1a/Lrr79mON6nTx8sFkuGrU2bNneUTSQveLpJOTrWLkW6zWDg3O2cuJBodqQst3DbKUInr+dYTCIlvFyZ/0wwT95fVoU5k1gsFlr8PbRVq7aK3ChTxTl/f//b2kQkd3JysDKmfXU+7hqIi6OVVQfP02HyOg5GXTY7moiI5FAzZ87k6tWrN+y/evUqs2bNyvT55s+fz4gRIxgzZgzbt28nMDCQkJAQzp27+V/mNmzYQPfu3enbty87duwgNDSU0NBQ9uzZk6FdmzZtOHv2rH377rvvMp1NJK+wWCyM71STQL9CxF5J5elZW0lITjM7VpZISk1n5A+7eGHBTpJSbTSp6M3SIU0I8i9sdrR8r3nl4gD8deBcvpnvUOR2Zao4FxMTw4kTJzLs27t3L08++SSPPfYYc+fOzdJwImKOjrVL88OARpQqVIATF64QOnk9S3adMTuWiIjkIPHx8cTFxWEYBpcvXyY+Pt6+Xbp0iV9//ZXixYtn+rwfffQRTz/9NE8++STVqlVjypQpuLm5MX369Ju2nzhxIm3atOHFF1+katWqjBs3jjp16vDZZ59laOfi4oKvr699K1z41n9RT05OznA/8fHxmb4PkZzO1cmBqU8EUbygC4eiExg+PxybLXcXTE5cSKTzFxuYt+UkFgsMb1WJGU/Wp4i7s9nRBLi/gjdODhYiL14hIibvPa0pcjcyVZwbPHgwn376qf31uXPnaNKkCVu2bCE5OZk+ffrw7bffZnlIEbn3apTy4pfBjWlcwZurqekMmruD8b/tJy3dZnY0ERHJAQoVKkSRIkWwWCxUqlSJwoUL2zdvb2+eeuopBg4cmKlzpqSksG3bNlq1amXfZ7VaadWqFWFhYTd9T1hYWIb2ACEhITe0X7VqFcWLF6dy5coMGDCACxcu3DLH+PHj8fLysm9+fn6Zug+R3MLH05UvnwjC2dHK8n3RfPznIbMj3bHf90bx8KR17D0TTxF3Z2Y9VZ+hrSpq/uQcxMPFkfpliwCw8uB5k9OI5CyZKs5t3LiRDh062F/PmjWLIkWKEB4ezs8//8w777zD5MmTszykiJijiLszM56sxzPNygHw5epj9PlmC5cSU0xOJiIiZlu5ciUrVqzAMAwWLlzIX3/9Zd/WrVtHZGQkr776aqbOGRMTQ3p6Oj4+Phn2+/j4EBUVddP3REVF/Wf7Nm3aMGvWLFasWMF7773H6tWradu27S0XMRs1ahRxcXH27eTJk5m6D5HcpHaZwozvWBO4NudwbhstkZpu451f9/PMt9u4nJRGnTKFWDqkMU0qasGBnKjF30NbVx3UvHMi/+SYmcZRUVEEBATYX//111906tQJR8drp+nQoQPjx4/P0oAiYi5HByuj2lalZikvXlywi3VHYmj/2Tq+fCKI6iW9zI4nIiImadasGXBtwbAyZcrcdJL1yMhIypQpc6+j3aBbt272f65Zsyb33Xcf5cuXZ9WqVbRs2fKG9i4uLri4uNzLiCKm6hxUmv1n4/lqXQQvLNhJQFF3apTK+f286PgkBs3dzpbjlwDo27gsI9tWwckhU8+gyD3UvHJx3lq6n03HLpKYnIa7S6ZKEiJ5Vqb+1PL09CQ2Ntb+evPmzTRo0MD+2mKxkJycnGXhRCTnePi+kvw0sBH+Rd04dekqnb/YwKIdp82OJSIiJitXrhznz984POnChQuULVs2U+fy9vbGwcGB6OjoDPujo6Px9fW96Xt8fX0z1f56Zm9vb44cOZKpfCJ52ci2VWhaqRhJqTb6z9pKTELO/nvdhiMxtPt0LVuOX8LDxZEvetbh9YerqTCXw5Uv5o5fkQKkpNvYcPTW0wuI5DeZ+pOrYcOGfPrpp9hsNhYuXMjly5d54IEH7McPHTqkOTlE8rAqvp4sHtiY5pWvddyGzQ/nzV/2kap56ERE8q1brbiXkJCAq6trps7l7OxMUFAQK1assO+z2WysWLGC4ODgm74nODg4Q3uA5cuX37I9wKlTp7hw4QIlSpTIVD6RvMzRwcqkbrUp6+3OmbgkBszeRkpazuvj2WwGn/11mMe/3kRMQgpVfAvyy+DGtK2p/z/nBhaLhQf+Htq6UkNbRewy9Qzpm2++SatWrZg9ezZpaWm88sorGVa6mjdvnn2Ig4jkTV5uTnzdux4fLz/EZyuPMH19BHvPxDG5Zx28PTQESEQkvxgxYgRw7S9ao0ePxs3NzX4sPT2dTZs2UatWrTs6b+/evalbty7169fnk08+ITExkSeffBKAXr16UapUKftUKkOHDqVZs2ZMmDCBdu3aMW/ePLZu3crUqVOBa0XCN954g86dO+Pr68vRo0d56aWXqFChAiEhIXf5KYjkLV5uTkzrVZeOk9ez5fglxizewzsda9502LoZLiWmMPz7cFb9vZjAo0GlGRdaA1cnB5OTSWY0r1KcmWEnWHXgHIZh5Jh/v0TMlKniXGBgIPv372f9+vX4+vpmGNIK1+bzqFatWpYGFJGcx8Fq4YWQytQo5cXz34ezKeIi7SetY8rjQQT6FTI7noiI3AM7duwArj05t3v3bpydne3HnJ2dCQwM5IUXXsj0ebt27cr58+cZPXo0UVFR1KpVi2XLltkXfYiMjMRq/f/BH40aNWLu3Lm89tprvPLKK1SsWJFFixZRo0YNABwcHNi1axczZ84kNjaWkiVL0rp1a8aNG6d55URuokJxDz7tXpunZm7hu80nqVrCk17BAWbHIvxkLAPnbOd07FVcHK2Me6QGj9XTqK3cKLhcUVwcrZyJS+JQdAKVfQuaHUnEdBbjVmMRbiIsLIwLFy7w8MMP2/fNmjWLMWPGkJiYSGhoKJMmTcqXHZ34+Hi8vLyIi4vD09PT7Dgi98yRc5fp/+02jp1PxNnRyluhNXisrjpKIiK3K7f3IZ588kkmTpyYK7Pfrtz+HYnciSmrj/LubwdwsFr4tm99GpX3NiWHYRh8u/EE45bsIzXdIKCoG5/3DKJaSf1/MTd78pvNrDx4npFtq/Bss/JmxxHJNrfbh8jUnHNvvvkme/futb/evXs3ffv2pVWrVowcOZJffvlFq7WK5DMVihdk0cD7aVXVh5Q0Gy8t3MXri/bkyDlKREQk633zzTd4enpy5MgRfv/9d65evQrcei46EckdnmlajtBaJUm3GQycs52TF6/c8wyJyWkMmRfO6J/3kppu0Ka6L4sHN1ZhLg9oUeXavHN/HdC8cyKQyeJceHh4huXm582bR4MGDZg2bRojRozg008/5fvvv8/ykCKSs3m6OjH1iSBGPFgJiwW+3XiCHtM2ci4+yexoIiKSzS5evEjLli2pVKkSDz30EGfPngWgb9++PP/88yanE5E7ZbFYeLfzfdxX2otLV1LpN3MrCclp9+z6h6Iv0+Gzdfyy8wyOVguvtavKF4/XwdPV6Z5lkOzTvNK14ty2E5eIu5pqchoR82WqOHfp0iX7fB8Aq1evpm3btvbX9erV4+TJk1mXTkRyDavVwpCWFfm6d10Kujqy9cQlHp60jm0nLpkdTUREstGwYcNwcnIiMjIyw6IQXbt2ZdmyZSYmE5G75erkwJdPBOHt4cLB6MuMmB+OzZb9T8Uu2nGaRz5bz9Hzifh4ujCvf0P6NSmnhQPykDJF3ShfzJ10m8G6wzFmxxExXaaKcz4+PkRERACQkpLC9u3badiwof345cuXcXLSLxki+dkDVXxYPKgxFYt7cO5yMt2mhjFn0wmzY4mISDb5448/eO+99yhdunSG/RUrVuTECf35L5LblfAqwJdPBOHsYOWPfdF8suJwtl0rKTWdV3/azbD54VxNTadxBW+WDmlC3YAi2XZNMU+Lyteenlt5UENbRTJVnHvooYcYOXIka9euZdSoUbi5udGkSRP78V27dlG+vCZzFMnvynq7s2jg/TxU05fUdINXf9rDyB92kZyWbnY0ERHJYomJiRmemLvu4sWL+XKRMJG8KMi/MG91vLYC8qcrDvPb7rNZfo2TF6/w6JQw5myKxGKBIS0rMvOp+nh76M+RvOr6vHOrDp6/J09kiuRkmSrOjRs3DkdHR5o1a8a0adOYNm0azs7O9uPTp0+ndevWWR5SRHIfdxdHJveow8ttqmC1wLwtJ+n65UbOxl01O5qIiGShJk2aMGvWLPtri8WCzWbj/fffp0WLFiYmE5Gs9FhdP566vywAI77fyb4z8Vl27j/3RdPu07XsPh1HYTcnvulTjxEPVsLBqmGseVndgMK4OzsQk5DM3iz890kkN7IYd7CUVlxcHB4eHjg4OGTYf/HiRTw8PDIU7PKL210eVyQ/WnPoPIO/20Hc1VS8PZyZ3KMODcoVNTuWiEiOkNv7EHv27KFly5bUqVOHv/76iw4dOrB3714uXrzI+vXr88Soitz+HYlklbR0G0/O2MLawzGUKlSAxYPup+hdPNmWlm7jwz8OMWX1UQBq+RVics86lCpUIKsiSw7Xf9ZW/tgXzYgHKzGkZUWz44hkudvtQ2TqybnrvLy8bijMARQpUiRfFuZE5N81rVSMXwY1popvQWISUuj51SZmrI/gDn4bEBGRHKZGjRocOnSIxo0b88gjj5CYmEinTp3YsWNHnijMicj/c3SwMql7bfyLunE69ioD5mwnJc12R+c6dzmJnl9tshfm+jQK4PtnglWYy2ceqKJ550TgDp+ckxvpF1WR/3YlJY2RP+xm8c4zALSsUpxxoTUoqU6YiORj6kPkfPqORDI6HH2Zjp9vICE5jZ4NyvB2x5qZev/GYxcY/N0Ozl9Oxt3Zgfe63MfD95XMprSSk0XFJdFw/AosFtj22oMUcdfDPpK33G4fwvEeZhKRfM7N2ZGJ3WpxX2kv3lt2gBUHzrHxo9W83LYKjzfwx6p5RUREcp01a9b86/GmTZveoyQicq9U9CnIxG616DdrK3M2RVK1hCePN/T/z/fZbAZfrjnGB78fwGZAJR8Pvng8iPLFPO5BasmJfL1cqVrCk/1n41lz6DyhtUuZHUnEFCrOicg9ZbFY6NekHM0qFWPkj7vZduISo3/ey6Idp3mv831U9ClodkQREcmE5s2b37DPYvn/H1vS07VSt0he1LKqDy+0rswHvx9k7OK9VCjuQcN/mVM47koqI74PZ8WBa8MXO9UuxVsda+DmrL+S5nctKhdj/9l4Vh48p+Kc5Ft3NOeciMjdquhTkAXPBDPukeq4OzuwPTKWhz5dyyd/HiI5TX+RExHJLS5dupRhO3fuHMuWLaNevXr88ccfZscTkWz0XPPytA8sSZrN4Lk52zl58cpN2+06FUu7SWtZceAczo5WxneqyYTHAlWYEwBa/D3v3OpD50m3adYtyZ9UnBMR01itFp4IDmD5iGa0rFKc1HSDT/48zMOfrmPbiYtmxxMRkdvg5eWVYfP29ubBBx/kvffe46WXXjI7nohkI4vFwvud76NGKU8uJqbw9KytJCan2Y8bhsHsjSfo8kUYpy5dxa9IAX4c0Iju9ctkeMJW8rfafoXwdHUk9koq4ScvmR1HxBQqzomI6UoWKsBXvevyWY/aeHs4c/hcAl2mhDH65z0k/KODJyIiuYePjw8HDx40O4aIZLMCzg5MfaIu3h7OHIi6zAsLdmKzGVxJSWP4/HBeW7SHlHQbD1bzYcngJtQo5WV2ZMlhHB2sNK1UDICVB86bnEbEHFqtNYtoFS+RrBF7JYW3l+5nwbZTAJTwcuWt0Bq0rOpjcjIRkeyR2/sQu3btyvDaMAzOnj3Lu+++S1paGuvWrTMpWdbJ7d+RyL2w9fhFuk/bSGq6wRMN/dl47AKHzyXgYLXwUkhl+jctp6fl5JZ+2HaK5xfspHpJT5YOaWJ2HJEso9VaRSRXKuTmzAePBvJIrVK88tNuIi9eoe/MrTx8XwnGtK9OsYIuZkcUEZF/qFWrFhaLhf/9vbdhw4ZMnz7dpFQicq/VDSjCW6E1ePmH3Xy78QQAxQu6MKl7bRr8y0IRIgDNKl97cm7vmXii45Pw8XQ1OZHIvaXinIjkSI0revP7sKZ88uchpq09xpJdZ1l7OIbX2lWlS1Bp/fIqIpJDREREZHhttVopVqwYrq76i5VIftO1XhkORiUwfX0EweWKMrF7LYoX1J8F8t+8PVwILO3FzlNxrD54nsfq+ZkdSeSe0rDWLKLhDiLZZ8/pOF5auIt9Z+MBaFzBm3c61qRMUTeTk4mI3D31IXI+fUcimXPy4hVKFSqA1aofU+X2fbz8EBNXHKZtDV++eDzI7DgiWeJ2+xBaEEJEcrwapbz4edD9jGxbBRdHK+uOxND6k9VMXXOUtHSb2fFERPK91atX0759eypUqECFChXo0KEDa9euNTuWiJjEr4ibCnOSaQ9UKQ7A2sMxpKqPL/mMinMikis4OVh5tll5fh/WlOByRUlKtfHOrwcI/Xw9e8/EmR1PRCTfmj17Nq1atcLNzY0hQ4YwZMgQChQoQMuWLZk7d67Z8UREJJeoWcqLou7OJCSnsfX4JbPjiNxTGtaaRTTcQeTeMQyDBVtP8dbSfcQnpeFgtfB0k3IMa1URVycHs+OJiGRKbu9DVK1alf79+zN8+PAM+z/66COmTZvG/v37TUqWdXL7dyQikluM+D6cH7ef5pmm5Rj1UFWz44jctVwxrHXNmjW0b9+ekiVLYrFYWLRoUYbjFovlptsHH3xgb3Px4kV69uyJp6cnhQoVom/fviQkJGQ4z65du2jSpAmurq74+fnx/vvv35BlwYIFVKlSBVdXV2rWrMmvv/6aLfcsInfPYrHwWD0//ny+Ge1qliDdZjBl9VHafLKGDUdjzI4nIpKvHDt2jPbt29+wv0OHDjcsFiEiIvJvWlS+NrR15cFzJicRubdMLc4lJiYSGBjI5MmTb3r87NmzGbbp06djsVjo3LmzvU3Pnj3Zu3cvy5cvZ8mSJaxZs4b+/fvbj8fHx9O6dWv8/f3Ztm0bH3zwAWPHjmXq1Kn2Nhs2bKB79+707duXHTt2EBoaSmhoKHv27Mm+mxeRu1a8oCuTe9Zh6hNB+Hq6cvzCFXpM28TLC3cRdyXV7HgiIvmCn58fK1asuGH/n3/+iZ+fVtsTEZHb17RiMawWOBSdwKlLV8yOI3LP5JhhrRaLhZ9++onQ0NBbtgkNDeXy5cv2DuD+/fupVq0aW7ZsoW7dugAsW7aMhx56iFOnTlGyZEm++OILXn31VaKionB2dgZg5MiRLFq0iAMHDgDQtWtXEhMTWbJkif1aDRs2pFatWkyZMuW28mu4g4i5Liel8v6yg3y78QRwbTn2Nx+pTtsavlgsmpBYRHKu3N6H+OKLLxg2bBhPPfUUjRo1AmD9+vXMmDGDiRMn8swzz5ic8O7l9u9IRCQ3eXTKBrYcv8S40Bo80dDf7DgidyVXDGvNjOjoaJYuXUrfvn3t+8LCwihUqJC9MAfQqlUrrFYrmzZtsrdp2rSpvTAHEBISwsGDB7l06ZK9TatWrTJcLyQkhLCwsFvmSU5OJj4+PsMmIuYp6OrEuNAaLHg2mPLF3IlJSOa5Odvp/+02ouKSzI4nIpJnDRgwgHnz5rF7926GDRvGsGHD2LNnD/Pnz88ThTkREbm3mv89tHXVAQ1tlfwj1xTnZs6cScGCBenUqZN9X1RUFMWLF8/QztHRkSJFihAVFWVv4+Pjk6HN9df/1eb68ZsZP348Xl5e9k3DNkRyhnoBRVg6pAlDHqiAk4OF5fuiefCj1czeeAKbLUc8KCwikud07NiRdevWceHCBS5cuMC6det45JFHzI4lIiK50PV559YfjSEpNd3kNCL3Rq4pzk2fPp2ePXvi6upqdhQARo0aRVxcnH07efKk2ZFE5G+uTg6MaF2ZJYObUMuvEJeT03ht0R66Tg3jyLmE/z6BiIhkWkpKCqdOnSIyMjLDJiIikhlVSxTEx9OFpFQbmyIumh1H5J7IFcW5tWvXcvDgQfr165dhv6+vL+fOZXzUNS0tjYsXL+Lr62tvEx0dnaHN9df/1eb68ZtxcXHB09MzwyYiOUtl34L8MKARY9tXw83ZgS3HL/HQxLVMWnGYlDSb2fFERPKEw4cP06RJEwoUKIC/vz9ly5albNmyBAQEULZsWbPjiYhILmOxWP5/1VYNbZV8IlcU577++muCgoIIDAzMsD84OJjY2Fi2bdtm3/fXX39hs9lo0KCBvc2aNWtITf3/lRuXL19O5cqVKVy4sL3N/64ytnz5coKDg7PrlkTkHnGwWuhzf1n+GN6U5pWLkZJuY8LyQ7SftI4dkZfMjicikuv16dMHq9XKkiVL2LZtG9u3b2f79u3s2LGD7du3mx1PRERyIfu8cwdVnJP8wdHMiyckJHDkyBH764iICMLDwylSpAhlypQBrq1ssWDBAiZMmHDD+6tWrUqbNm14+umnmTJlCqmpqQwaNIhu3bpRsmRJAHr06MEbb7xB3759efnll9mzZw8TJ07k448/tp9n6NChNGvWjAkTJtCuXTvmzZvH1q1bmTp1ajZ/AiJyr5Qu7MY3feqxeOcZ3vhlHwejL9Ppiw30aRTAC60r4+5i6h+HIiK5Vnh4ONu2baNKlSpmRxERkTyicUVvnBwsHL9whYiYRMp6u5sdSSRbmfrk3NatW6lduza1a9cGYMSIEdSuXZvRo0fb28ybNw/DMOjevftNzzFnzhyqVKlCy5Yteeihh2jcuHGGopqXlxd//PEHERERBAUF8fzzzzN69Gj69+9vb9OoUSPmzp3L1KlTCQwMZOHChSxatIgaNWpk052LiBksFguP1CrFnyOa0alOKQwDvll/nNYfr9GvciIid6hatWrExMSYHUNERPIQDxdH6pctAmhoq+QPFsMwtHxhFoiPj8fLy4u4uDjNPyeSS6w5dJ5XftrNqUtXAQitVZLXH65GUQ8Xk5OJSH6SG/sQ8fHx9n/eunUrr732Gu+88w41a9bEyckpQ9vcck//Jjd+RyIiud1Xa4/x1tL9NKnozbd9G5gdR+SO3G4fQsW5LKJOm0judCUljY/+OMT09RHYDCjs5sTo9tUIrVUKi8VidjwRyQdyYx/CarVm+DPSMIwb/sy8vi89Pf1ex8tyufE7EhHJ7Y6cS6DVR6txdrASPuZB3Jw1DY3kPrfbh9C/3SKSr7k5O/Law9VoH1iSl3/YxYGoywyfv5Ofdpzh7dAa+BVxMzuiiEiOs3LlSrMjiIhIHle+mDt+RQpw8uJV1h+5wIPVfMyOJJJtVJwTEQEC/Qrxy+DGTF1zjIkrDrPm0Hlaf7yGF0Iq06dRAA5WPUUnInJds2bNzI4gIiJ5nMVioUXl4swKO8HKg+dUnJM8TcU5EZG/OTlYGdiiAm1r+DLqx/9r787joir3P4B/ZmEGRBbZRTZXcEFwRdwA5YpmplmmZu51s9RcSelmWt4CrUxLy6uVmuXaL83dCAUXcAFFRQUVEVDZXGBYZJs5vz/Iuc0VE5PhDMPn/Xqd15VznjnzOUw3v33nPM+5gJNp97B4zyXsSryFiJc6om1TTmUiIgKA8+fP13hsx44d9ZiEiIiM2cPmXHRybrVLKBAZCzbniIj+Rwv7xtj8Rg9sjc/EJ/su49zNAgz56himBLTEtH6tYGoiEzsiEZGofH19IZFI8KSli41lzTkiIhJHjxa2UMqluF1Qiis5RfB0shA7EpFesDlHRFQNqVSC0d3d0N/LAQt3XcT+pGysPHwN+y5kIXy4N/xa2IodkYhINGlpaWJHICKiBsBMIYN/S1tEp+ThcEoum3NktNicIyL6Cw6WpvjmtS44kJSND35NwvU7xRi55gRe9XPD/EFesDQ1ETsiEVGdc3d3FzsCERE1EEGeDlXNueRcTAloKXYcIr1gc46IqAYGdnCCf0tbROxPxuZTGdh0MgO/X8rB4mEdENLeSex4RER1ateuXRg0aBBMTEywa9euvxz7wgsv1FEqIiIyRkGeDliIi4hPvw9VaQW/HCejJBGetFgI1YhKpYKVlRUKCgpgaclF44mM2cnrdxH2ywVcv1MMABjUwQkfvtAeDpamIicjovqoPtYQUqkU2dnZcHBwgFQqfew4Y1lzrj5+RkRExqTf59G4nleMr8d0xnPeTcWOQ1RjNa0hHl9NERFRtfxa2GLfjD6YFtQKcqkE+5Oy0X9ZDLacynji4uhERMZAo9HAwcFB++fHbcbQmCMiIvH186z6O+dwcq7ISYj0g805IqK/wdREhrkhntg9vTd8XKxQWFqJ+b9cwOi1J5D2xx11RERERET07IK8qppz0VfyoNHwy3AyPmzOERE9g7ZNLfHL272w4Pl2MDOR4cT1ewhZfgRfR19DhVojdjwiIr2Ji4vDnj17dPb98MMPaN68ORwcHPDPf/4TZWVlIqUjIiJj0tWjCcwVMuQVluFSlkrsOES1js05IqJnJJNKMLl3c/w2qy/6trFHeaUGSw+k4IWVx3H+Zr7Y8YiI9OKjjz7CxYsXtT9fuHABkydPRnBwMObPn4/du3cjPDxcxIRERGQslHIZerWyAwAc4tRWMkJszhER1RJXm0bYMLEbvhjpgyaNTHA5S4Vhq47j472XUFJeKXY8IqJalZiYiP79+2t/3rJlC/z8/LB27VrMnj0bX375JbZt2/a3zr1q1Sp4eHjA1NQUfn5+OHXq1F+O3759O7y8vGBqagpvb2/s27fvsWOnTJkCiUSC5cuX/61sREQkjodTWw+nsDlHxofNOSKiWiSRSPBiJxf8PjsAw3ydoRGAtUfTELL8CH6/lMMHRhCR0bh//z4cHR21P8fExGDQoEHan7t164bMzMynPu/WrVsxe/ZsLFy4EGfOnIGPjw9CQkKQm1v9f4zFxsZi9OjRmDx5Ms6ePYthw4Zh2LBhSEpKemTsjh07cOLECTg7Oz91LiIiElegpz0AIDEzH/eKy0VOQ1S72JwjItID28ZKLB/VCesmdkMzazNk3nuA13+Ix2vfncRlrpNBREbA0dERaWlpAIDy8nKcOXMGPXr00B4vLCyEiYnJU5932bJleOONNzBx4kS0a9cOq1evRqNGjfD9999XO37FihUYOHAgQkND0bZtWyxevBidO3fGypUrdcbdunUL06dPx08//fS3chERkbiaWpnBy8kCggAcuZIndhyiWsXmHBGRHgV5OuC3WX0xNaglFHIpjl+7i8FfHkXYLxdwp4gLpRNR/fXcc89h/vz5OHr0KMLCwtCoUSP06dNHe/z8+fNo2bLlU52zvLwcCQkJCA4O1u6TSqUIDg5GXFxcta+Ji4vTGQ8AISEhOuM1Gg3Gjh2L0NBQtG/f/ok5ysrKoFKpdDYiIhIfp7aSsWJzjohIz8yVcoSGeCFqdgAGd2wKjQBsPpWBwE+jsTomFWWVarEjEhE9tcWLF0MulyMgIABr167F2rVroVAotMe///57DBgw4KnOeefOHajVap3pskDVXXrZ2dnVviY7O/uJ45csWQK5XI533nmnRjnCw8NhZWWl3VxdXZ/qOoiISD+CPKuaczFX8qDWcLkYMh5ysQMQETUUrjaNsOrVzpjQ8x4W77mE8zcLELE/GZtOZiBskBcGdnCCRCIROyYRUY3Y2dnhyJEjKCgoQOPGjSGTyXSOb9++HY0bNxYp3X8lJCRgxYoVOHPmTI3/HRsWFobZs2drf1apVGzQEREZgM5u1rAwlSO/pAKJmfno4t5E7EhEtYJ3zhER1bFuHjbY+XYvfD7CB46WSmTcK8FbP53ByDUnkHSrQOx4RERPxcrK6pHGHADY2Njo3ElXE3Z2dpDJZMjJydHZn5OTAycnp2pf4+Tk9Jfjjx49itzcXLi5uUEul0MulyM9PR1z5syBh4dHtedUKpWwtLTU2YiISHxymRR921Q9GCKaU1vJiLA5R0QkAqlUgpe6uODw3EC80781TE2kOJV2D0NWHkPo9nPIVZWKHZGIqM4pFAp06dIFUVFR2n0ajQZRUVHw9/ev9jX+/v464wEgMjJSO37s2LE4f/48EhMTtZuzszNCQ0Nx8OBB/V0MERHpxcOprVx3jowJp7USEYmokUKO2f9og1HdXLH0QDJ2Jt7G9oSb2HshC28HtsTrfVrA1OTRO1KIiIzV7NmzMX78eHTt2hXdu3fH8uXLUVxcjIkTJwIAxo0bh2bNmiE8PBwAMGPGDAQEBODzzz/H4MGDsWXLFsTHx2PNmjUAAFtbW9ja2uq8h4mJCZycnODp6Vm3F0dERM8s0LPqzrmkWyrkqkrhYGkqciKiZ8c754iIDICztRmWj+qEHW/3RCc3a5SUq/HZb1fQ//MY7Dp3G4LABW+JqGEYOXIkPvvsM3zwwQfw9fVFYmIiDhw4oH3oQ0ZGBrKysrTje/bsiU2bNmHNmjXw8fHBzz//jJ07d6JDhw5iXQIREemRXWMlfFysAADRV/JETkNUOyQC/4uvVqhUKlhZWaGgoIDrkhDRMxEEAbvPZyFi32XcLqia3trFvQkWPN8Ovq7W4oYjolrHGsLw8TMiIjIsX0RewYqoqxjUwQnfvNZF7DhEj1XTGoJ3zhERGRiJRIIXfJxxaG4g5vyjDRopZEhIv49hq45j1tZEZBU8EDsiEREREZFogryq1p07evUOKtQakdMQPTs254iIDJSpiQzT+7fG4bmBeLmLCyQSYMfZWwj6LBpfRF5BSXml2BGJiIiIiOpcx2ZWsDVXoKisEvE37osdh+iZsTlHRGTgHC1N8dkIH+ya2hvdPWxQWqHBiqir6PdZDH45cxMaDVcnICIiIqKGQyqVIKBN1YMhovnUVjICbM4REdUT3i5W2PpmD3wzpjNcbcyQrSrF7G3n8OLXx5GQfk/seEREREREdSbwj6mth9mcIyPA5hwRUT0ikUgwyLspImcFYN5ALzRWynHuZgFe+iYO0zadwc37JWJHJCIiIiLSu76t7SCVAFdyilgDU73H5hwRUT1kaiLDW4EtcXhuIEZ3d4VUAuw5n4V+n8fg04PJKCrjenREREREZLysGynQ2a0JACA6JU/kNETPhs05IqJ6zN5CifDhHbFneh/0bGmL8koNVh1ORdBn0dh2OhNqrkdHREREREbq4VNbue4c1XdszhERGYF2zpb46XU/rB3XFR62jZBXWIZ3/+88Xlh5DCeu3xU7HhERERFRrQv0rHooxPFrd1FaoRY5DdHfx+YcEZGRkEgk+Ec7R/w2KwDvD24LC1M5Lt5WYdSaE5iyMQHpd4vFjkhEREREVGvaNbWEo6USDyrUOJXGB6RR/cXmHBGRkVHIpXi9TwvEhAZhbA93yKQSHLiYjX8sO4LwfZehKq0QOyIRERER0TOTSCQI8uRTW6n+Y3OOiMhI2ZgrsHhYB+yf0Qd929ijXK3Bf45cR9Cn0fjpZDoq1RqxIxIRERERPZPAh825ZDbnqP5ic46IyMi1cbTAD5O6Y93Ebmhpb467xeX4144kPP/VMRy7ekfseEREREREf1uvVrYwkUlw424J0u5wGReqn9icIyJqIII8HXBgZl98+EJ7WDcyQXJ2IV777iQmrz+N1LwiseMRERERET01C1MTdPOwAcC756j+YnOOiKgBMZFJMb6nB6LnBmJiLw/IpRJEJeci5Isj+Gj3JRSUcD06IiIiIqpfuO4c1XdszhERNUDWjRRYOKQ9Ds7qi/5eDqjUCPj+eBoCPjuMDbE3UMH16IiIiIiongjysgcAnLx+DyXllSKnIXp6bM4RETVgLe0b47sJ3bBxcnd4Ologv6QCC3ddxMDlR/jNIxERERHVCy3tG8OliRnK1RrEXrsrdhyip8bmHBERoU9re+x9pzc+frEDbMwVSM0rxsR1pzHu+1O4klModjwiIiIioseSSCSc2kr1mqjNuSNHjmDIkCFwdnaGRCLBzp07Hxlz+fJlvPDCC7CysoK5uTm6deuGjIwM7fHAwEBIJBKdbcqUKTrnyMjIwODBg9GoUSM4ODggNDQUlZW6t7pGR0ejc+fOUCqVaNWqFdavX6+PSyYiMlhymRRj/NwRHRqIN/u2gIlMgiNX8jBoxVEs2JmEe8XlYkckIiIiIqrWw6mt0Sl5EARB5DRET0fU5lxxcTF8fHywatWqao+npqaid+/e8PLyQnR0NM6fP48FCxbA1NRUZ9wbb7yBrKws7bZ06VLtMbVajcGDB6O8vByxsbHYsGED1q9fjw8++EA7Ji0tDYMHD0ZQUBASExMxc+ZMvP766zh48KB+LpyIyIBZmpog7Lm2iJwVgJD2jlBrBGw8kY6ATw/j26PXUV7J9eiIiIiIyLD4t7CDUi7FrfwHuJpbJHYcoqciEQykpSyRSLBjxw4MGzZMu2/UqFEwMTHBxo0bH/u6wMBA+Pr6Yvny5dUe379/P55//nncvn0bjo6OAIDVq1dj3rx5yMvLg0KhwLx587B3714kJSXpvHd+fj4OHDhQ7XnLyspQVlam/VmlUsHV1RUFBQWwtLR8iisnIjJscal3sXjPJVzKUgEAmtuZ473n2iK4rQMkEonI6YjqP5VKBSsrK9YQBoyfERFR/TBh3SlEp+QhbJAX3gxoKXYcohrXEAa75pxGo8HevXvRpk0bhISEwMHBAX5+ftVOff3pp59gZ2eHDh06ICwsDCUlJdpjcXFx8Pb21jbmACAkJAQqlQoXL17UjgkODtY5Z0hICOLi4h6bLzw8HFZWVtrN1dX1Ga+YiMgw+be0xe7pvbH0pY6wa6xE2p1ivPFDPMZ8exKX/2jYERERERGJjevOUX1lsM253NxcFBUVISIiAgMHDsRvv/2GF198EcOHD0dMTIx23Kuvvooff/wRhw8fRlhYGDZu3IjXXntNezw7O1unMQdA+3N2dvZfjlGpVHjw4EG1+cLCwlBQUKDdMjMza+W6iYgMkUwqwSvdXBEdGoipQS2hkEsRm3oXg788irBfziOvsOzJJyEiIiIi0qOHzbn4G/ehKq0QOQ1RzcnFDvA4Gk3VmkZDhw7FrFmzAAC+vr6IjY3F6tWrERAQAAD45z//qX2Nt7c3mjZtiv79+yM1NRUtW+rvNlalUgmlUqm38xMRGaLGSjlCQ7wwqpsbIg4kY+/5LGw+lYnd57IwrV8rTOzlAaVcJnZMIiIiImqA3GwboYW9Oa7nFePY1Tt4zrup2JGIasRg75yzs7ODXC5Hu3btdPa3bdtW52mt/8vPzw8AcO3aNQCAk5MTcnJydMY8/NnJyekvx1haWsLMzOzZLoSIyAi52jTCqlc7Y/sUf3R0sUJRWSUi9icjeFkM9l/I4hOyiIiIiEgU2qmtyZzaSvWHwTbnFAoFunXrhpSUFJ39V65cgbu7+2Nfl5iYCABo2rSqQ+7v748LFy4gN/e//8eMjIyEpaWltvHn7++PqKgonfNERkbC39+/Ni6FiMhodfOwwc63e2HZKz5wtFQi894DvPXTGYxccwLnMvPFjkdEREREDczD5lz0lTxoNPzCmOoHUZtzRUVFSExM1DbU0tLSkJiYqL0zLjQ0FFu3bsXatWtx7do1rFy5Ert378bbb78NAEhNTcXixYuRkJCAGzduYNeuXRg3bhz69u2Ljh07AgAGDBiAdu3aYezYsTh37hwOHjyI999/H1OnTtVOS50yZQquX7+Od999F8nJyfj666+xbds27XRaIiJ6PKlUguGdXXB4biBm9G8NUxMpTqXdw9BVx/H2Twm4nsdH2RMRERFR3ejWvAkaKWTIKyzDJT68jOoJiSDi3KPo6GgEBQU9sn/8+PFYv349AOD7779HeHg4bt68CU9PT3z44YcYOnQoACAzMxOvvfYakpKSUFxcDFdXV7z44ot4//33dR5Rm56ejrfeegvR0dEwNzfH+PHjERERAbn8v0vuRUdHY9asWbh06RJcXFywYMECTJgwocbXUtPH4xIRGbvb+Q/w+W9X8MvZmxCEPx4m0dUVM4Nbw9HSVOx4RAaHNYTh42dERFS/vPFDPCIv5WDOP9pgev/WYsehBqymNYSozTljwqKNiEhXSnYhPj2YjN8vVy0rYGoixaRezfFmQEtYmZmInI7IcLCGMHz8jIiI6pdNJzPw3o4L6OxmjV/e7iV2HGrAalpDGOyac0REVL95Olng2/HdsH2KP7q4N0FphQZfR6ci4NPDWHvkOkor1GJHJCIiIiIjFOhpDwA4m5mPe8XlIqchejI254iISK+6edjg5yn+WDuuK1o7NEZ+SQU+3ncZ/T6Lxrb4TKi5UC8RERER1SJnazN4OVlAEICjV/PEjkP0RGzOERGR3kkkEvyjnSMOzOyLT1/uCGcrU9wuKMW7P5/HoBVHEHkpB1xlgYiIiIhqS+AfT209nJwrchKiJ2NzjoiI6oxMKsGIrq44NDcQ/3quLazMTHAlpwhv/BCPEavjcPrGPbEjEhEREZER6OdV1ZyLuZLHmRpk8NicIyKiOmdqIsMbfVvgyLtBeDuwJUxNpIhPv48Rq+Pw+obTSMkuFDsiEREREdVjnd2sYWEqx/2SCiRm5osdh+gvsTlHRESisTIzwbsDvRATGoRX/dwgk0rw++VcDFxxBHO2ncPN+yViRyQiIiKiekguk6Jvm6oHQ0SncGorGTY254iISHSOlqb45EVv/DarL57zdoIgAP935ib6fRaDf++5hPt8yhYRERERPaWgh+vOsTlHBo7NOSIiMhgt7Rvj6zFd8OvUXvBvYYtytQbfHktD36WHsfLQVZSUV4odkYiIiIjqiYA/7pxLuqVCrqpU5DREj8fmHBERGRwfV2tsesMPGyZ1R7umligsq8Rnv11BwKfR+PFEOirUGrEjEhEREZGBs7dQoqOLFQAg+kqeyGmIHo/NOSIiMkgSiQQBbeyxZ3pvrBjlCzebRsgrLMP7O5Mw4Isj2HP+NgSBT94iIiIioscL/GNqK9edI0PG5hwRERk0qVSCob7N8PvsAHz4QnvYNVYg7U4xpm06i6GrjuP4tTtiRyQiIiIiAxXkWTW19eiVO5x9QQaLzTkiIqoXFHIpxvf0QHRoEGYFt4G5QobzNwsw5tuTGPvdSSTdKhA7IhEREREZmI4u1rAxV6CwrBIJ6ffFjkNULTbniIioXmmslGNGcGvEvBuECT09YCKT4OjVO3j+q2OYvvks0u8Wix2RiIiIiAyETCrRPhiCT20lQ8XmHBER1Ut2jZVY9EJ7HJoTiBc7NYNEAuw+dxv9P4/Bgp1JyC3kE7mIiIiICAj8Y2prdDIfCkGGic05IiKq11xtGuGLkb7YO70PAj3tUakRsPFEOgI/jcay31JQWFohdkQiIiIiElFAG3tIJUBKTiFu5T8QOw7RI9icIyIio9DO2RLrJ3bH5jd6wMfVGiXlanx56BoCPo3Gd8fSUFapFjsiEREREYnAupECnd2aAAAOJ3NqKxkeNueIiMio+Le0xc63e2L1a53Rwt4c94rLsXjPJfT7LAa/nLkJtUYQOyIRERER1bEgLwcAQDTXnSMDxOYcEREZHYlEgoEdmuK3mX0RPtwbjpZK3Mp/gNnbzmHwl0dxKDkHgsAmHZGhWrVqFTw8PGBqago/Pz+cOnXqL8dv374dXl5eMDU1hbe3N/bt26dzfNGiRfDy8oK5uTmaNGmC4OBgnDx5Up+XQEREBubhunPHr91FaQVnVJBhYXOOiIiMllwmxejuboieG4R5A71gaSpHcnYhJq2Px8g1J3Am477YEYnof2zduhWzZ8/GwoULcebMGfj4+CAkJAS5udXf6RAbG4vRo0dj8uTJOHv2LIYNG4Zhw4YhKSlJO6ZNmzZYuXIlLly4gGPHjsHDwwMDBgxAXh4XBiciaijaNbWEg4USDyrUOJV2T+w4RDokAm8dqBUqlQpWVlYoKCiApaWl2HGIiKga+SXl+CY6Fetib6C8UgMAGNDOEe8O9EQrBwuR01FDxRpCl5+fH7p164aVK1cCADQaDVxdXTF9+nTMnz//kfEjR45EcXEx9uzZo93Xo0cP+Pr6YvXq1dW+x8Pf+e+//47+/fs/MRM/IyIi4zDv5/PYGp+Jib08sHBIe7HjUANQ0xqCd84REVGDYd1IgbDn2iImNBAju7pCKgF+u5SDAV8cwbyfzyOrgE/vIhJTeXk5EhISEBwcrN0nlUoRHByMuLi4al8TFxenMx4AQkJCHju+vLwca9asgZWVFXx8fKodU1ZWBpVKpbMREVH9F+RVNbU1OoV3TpNhYXOOiIganKZWZljyckf8NqsvBrRzhEYAtsZnIvDTaITvu4z8knKxIxI1SHfu3IFarYajo6POfkdHR2RnZ1f7muzs7BqN37NnDxo3bgxTU1N88cUXiIyMhJ2dXbXnDA8Ph5WVlXZzdXV9hqsiIiJD0auVHeRSCdLuFCPtTrHYcYi02JwjIqIGq5WDBdaM64r/e6snunvYoKxSg/8cuY6+Sw/jm+hUPCjnYsFExiIoKAiJiYmIjY3FwIED8corrzx2HbuwsDAUFBRot8zMzDpOS0RE+mBhaoJuHjYA+NRWMixszhERUYPXxb0Jtr7ZA99P6AovJwuoSiux5EAyAj87jM2nMlCp1ogdkahBsLOzg0wmQ05Ojs7+nJwcODk5VfsaJyenGo03NzdHq1at0KNHD3z33XeQy+X47rvvqj2nUqmEpaWlzkZERMbh4dTWw5zaSgaEzTkiIiIAEokE/bwcsfedPlj2ig+aWZshR1WGsF8uYMDyIziQlAU+Q4lIvxQKBbp06YKoqCjtPo1Gg6ioKPj7+1f7Gn9/f53xABAZGfnY8X8+b1lZ2bOHJiKieiXI0wEAcOL6XZSUV4qchqgKm3NERER/IpNKMLyzCw7NDcCC59uhSSMTXM8rxpQfz+DFr2MRl3pX7IhERm327NlYu3YtNmzYgMuXL+Ott95CcXExJk6cCAAYN24cwsLCtONnzJiBAwcO4PPPP0dycjIWLVqE+Ph4TJs2DQBQXFyM9957DydOnEB6ejoSEhIwadIk3Lp1CyNGjBDlGomISDytHBqjmbUZyis1rOvIYLA5R0REVA2lXIbJvZsj5t0gTO/XCmYmMiRm5mP02hOYsO4ULt3m0xuJ9GHkyJH47LPP8MEHH8DX1xeJiYk4cOCA9qEPGRkZyMrK0o7v2bMnNm3ahDVr1sDHxwc///wzdu7ciQ4dOgAAZDIZkpOT8dJLL6FNmzYYMmQI7t69i6NHj6J9+/aiXCMREYmnarZE1d1zh5K57hwZBonAOTq1QqVSwcrKCgUFBVyXhIjICOUWluKrqGtVa9BpBEgkwFAfZ8wZ4AlXm0Zix6N6jDWE4eNnRERkXA4l52DS+ng0szbDsXlBkEgkYkciI1XTGoJ3zhEREdWAg4UpFg/rgN9nB+D5jk0hCMDOxNvo93k0Fu26iNzCUrEjEhEREVEN+Lewg0Iuxa38B7iaWyR2HCI254iIiJ6Gh505Vr7aGbun9Uaf1naoUAtYH3sDvZccxns7LuDGnWKxIxIRERHRXzBTyODfwhYAcJhTW8kAsDlHRET0N3i7WGHjZD/8ONkPnd2sUV6pwaaTGej3eTSmbTqDpFsFYkckIiIioscI8rQHABxOYXOOxMfmHBER0TPo3doO//dWT2x70x9BnvbQCMCe81l4/qtjGPf9KcSl3gWXdyUiIiIyLIGeVQ+FiL9xH6rSCpHTUEPH5hwREdEzkkgk6N7cBusmdse+d/pgqK8zpBLgyJU8jF57Ai9+HYsDSdnQaNikIyIiIjIEHnbmaGFnjkqNgONX74gdhxo4NueIiIhqUTtnS6wY1QnRc4Mwtoc7lHIpEjPzMeXHBPzjixhsi89EeaVG7JhEREREDd7Du+c4tZXExuYcERGRHrjZNsLiYR1wbF4/TA1qCQtTOVLzivHuz+cR8OlhfHv0OorLKsWOSURERNRgBXk9XHcuj8uQkKjYnCMiItIjewslQkO8EDu/H957zgsOFkpkFZTi33svo2fEISyLvIJ7xeVixyQiIiJqcLo3t4GZiQx5hWW4eFsldhxqwNicIyIiqgMWpib4Z9+WODovCBHDvdHczhwFDyrwZdRV9IyIwqJdF3HzfonYMYmIiIgaDKVchl6t7AAA0ZzaSiJic46IiKgOKeUyjOruht9nB+DrMZ3h3cwKpRUarI+9gYBPozF7ayJSsgvFjklERETUIPTzqlp37lAym3MkHrnYAYiIiBoimVSC57ybYlAHJ8Sm3sU30ak4du0Ofjl7C7+cvYXgtg54K7AlurjbiB2ViIiIyGgFelatO3c2Mx/3isthY64QORE1RLxzjoiISEQSiQS9Wtnhx9f9sGtaLzzn7QSJBPj9ci5e+iYOr6yOw6HkHC5STERERKQHztZm8HKygCAAR6/miR2HGig254iIiAxERxdrfD2mC6JmB2BUN1eYyCQ4deMeJq2Px6AVR7Hz7C1UqjVixyQiIiIyKoGeVVNbD3NqK4lE1ObckSNHMGTIEDg7O0MikWDnzp2PjLl8+TJeeOEFWFlZwdzcHN26dUNGRob2eGlpKaZOnQpbW1s0btwYL730EnJycnTOkZGRgcGDB6NRo0ZwcHBAaGgoKisrdcZER0ejc+fOUCqVaNWqFdavX6+PSyYiInqiFvaNEfFSRxx9tx/+2bcFzBUyJGcXYubWRAR+Fo0f4m7gQbla7JhERERERiHoj6mtMVfyoNZwtgLVPVGbc8XFxfDx8cGqVauqPZ6amorevXvDy8sL0dHROH/+PBYsWABTU1PtmFmzZmH37t3Yvn07YmJicPv2bQwfPlx7XK1WY/DgwSgvL0dsbCw2bNiA9evX44MPPtCOSUtLw+DBgxEUFITExETMnDkTr7/+Og4ePKi/iyciInoCJytTvPdcW8TO74/QEE/Ymitw8/4DfPDrRfRecggrD11FQUmF2DGJiIiI6rXO7k1gYSrH/ZIKnLuZL3YcaoAkgoEsYiORSLBjxw4MGzZMu2/UqFEwMTHBxo0bq31NQUEB7O3tsWnTJrz88ssAgOTkZLRt2xZxcXHo0aMH9u/fj+effx63b9+Go6MjAGD16tWYN28e8vLyoFAoMG/ePOzduxdJSUk6752fn48DBw5U+95lZWUoKyvT/qxSqeDq6oqCggJYWlo+66+DiIjoEaUVamyPz8R/jlzHzfsPAADmChnG9HDH5N7N4Whp+oQzkCFSqVSwsrJiDWHA+BkRERm/qT+dwd4LWXinXyvMHuApdhwyEjWtIQx2zTmNRoO9e/eiTZs2CAkJgYODA/z8/HSmviYkJKCiogLBwcHafV5eXnBzc0NcXBwAIC4uDt7e3trGHACEhIRApVLh4sWL2jF/PsfDMQ/PUZ3w8HBYWVlpN1dX19q4bCIioscyNZFhrL8HoucGYsUoX3g5WaC4XI01R66jz5LDmPfzeaTmFYkdk4iIiKjeefjU1sMpfCgE1T2Dbc7l5uaiqKgIERERGDhwIH777Te8+OKLGD58OGJiYgAA2dnZUCgUsLa21nmto6MjsrOztWP+3Jh7ePzhsb8ao1Kp8ODBg2rzhYWFoaCgQLtlZmY+8zUTERHVhFwmxVDfZtg/ow/WTeiG7s1tUK7WYGt8JoKXxeCtHxNwLjNf7JhERERE9UbAH825C7cKkFtYKnIaamjkYgd4HI2m6ml0Q4cOxaxZswAAvr6+iI2NxerVqxEQECBmPCiVSiiVSlEzEBFRwyaRSBDk5YAgLwckpN/DN9HX8fvlHOxPysb+pGz0amWLtwJaoVcrW0gkErHjEhERERksBwtTeDezwoVbBYhJycOIrpwdR3XHYO+cs7Ozg1wuR7t27XT2t23bVvu0VicnJ5SXlyM/P19nTE5ODpycnLRj/vfprQ9/ftIYS0tLmJmZ1do1ERER6UsXdxt8O74rfpvVF8M7N4NcKsHxa3fx2ncn8cLK49h3IYtPHyMiIiL6Cw+f2hrNqa1Uxwy2OadQKNCtWzekpKTo7L9y5Qrc3d0BAF26dIGJiQmioqK0x1NSUpCRkQF/f38AgL+/Py5cuIDc3FztmMjISFhaWmobf/7+/jrneDjm4TmIiIjqizaOFlj2ii+iQwMxoacHzExkuHCrAG//dAbBy2Kw+VQGyirVYsckIiIiMjhBXg4AgCNX8lCh1oichhoSUae1FhUV4dq1a9qf09LSkJiYCBsbG7i5uSE0NBQjR45E3759ERQUhAMHDmD37t2Ijo4GAFhZWWHy5MmYPXs2bGxsYGlpienTp8Pf3x89evQAAAwYMADt2rXD2LFjsXTpUmRnZ+P999/H1KlTtdNSp0yZgpUrV+Ldd9/FpEmTcOjQIWzbtg179+6t898JERFRbXBp0giLXmiPd/q3xobYG1gfewNpd4oR9ssFfBF5BZN7N8erfm6wMDUROyoRERGRQejoYg0bcwXuFZcjIf0+erSwFTsSNRASQRBEm+MSHR2NoKCgR/aPHz8e69evBwB8//33CA8Px82bN+Hp6YkPP/wQQ4cO1Y4tLS3FnDlzsHnzZpSVlSEkJARff/21dsoqAKSnp+Ott95CdHQ0zM3NMX78eEREREAu/29vMjo6GrNmzcKlS5fg4uKCBQsWYMKECTW+lpo+HpeIiEgMxWWV2HI6E98evY6sgqpFji1M5Rjn744JPZvD3oLrqIqFNYTh42dERNRwzNqaiB1nb+HNgBYIG9RW7DhUz9W0hhC1OWdMWLQREVF9UF6pwa+Jt7A6JhWpecUAAKVcile6uuKNPi3gZttI5IQND2sIw8fPiIio4fg18RZmbEmEp6MFDs7qK3YcqudqWkMY7JpzREREVPsUcilGdHVF5KwA/GdsF/i6WqOsUoONJ9IR+NlhvLP5LC7dVokdk4iIiEgUfVvbQyoBUnIKcSv/gdhxqIFgc46IiKgBkkolCGnvhB1v98SWf/ZAQBt7aARg17nbeO7Lo5iw7hROXr8L3mBPREREDUkTcwU6uTUBAESn5D5hNFHtYHOOiIioAZNIJOjRwhYbJnXHnum9McTHGVIJEJ2Sh5FrTuClb2IReSkHGg2bdERERNQwBHnaAwAOJ+eJnIQaCjbniIiICADQoZkVvhrdCYfnBmKMnxsUcinOZOTjjR/iEbL8CH5OuIkKtUbsmERERER6FejpAAA4fu0OyirVIqehhkD+5CFERETUkLjbmuPjF70xI7g11h+/gY1x6biaW4S5289h2W8peL1PC4zs5gpzJcsIIiIiMj7tnS3hYKFEbmEZTqXdQ5/W9mJHeiaCIOBBhRr5JRW4X1KO/JIK7Z8LHlTgfnE58h9UIL+kHIWllejk1gRj/d3RzNpM7OgNBp/WWkv4FC8iIjJWqtIKbDqZge+OpSGvsAwAYKGU4+WuLhjn74HmduYiJ6zfWEMYPn5GREQNz7s/n8O2+JuY1Ks5PhjSTuw4WqUV6qqGmrbJVv5Ho60C+Q/KkV9c9b/3SypQ8LAZ96AC5ZVPN/tBJpUgpL0jJvZqjq7uTSCRSPR0RcatpjUEm3O1hEUbEREZu9IKNX45cwtrj15H2p1i7f6ANvaY0NMDAW3sIZWycHtarCEMHz8jIqKGZ/+FLLz10xm0sDPHobmBtX7+CrUG+SUVKPijkaa9k+1PDTWdxtsff35Q8fen2ZrIJLAyU6BJIxNYNzKBdSMFrM1M0MRcUfWzmQJyqQQ7zt5C3PW72td1aGaJCT2bY4hPUyjlstq4/AaDzbk6xqKNiIgaCo1GwNFrd/BD7A0cSsnFw0rC3bYRxvZwx4iurrAyMxE3ZD3CGsLw8TMiImp4VKUV6PxRJCo1AqLnBsLjMTMF1BoBqgfVN9QKSqoab9rpo3+aUlpUVvm3s0kl0DbWrBuZoEkjBaz++F9rMxNYm//RdGv0R9Ptj0acuUJW4zvgkrNVWH/8BnacvYWyP+66s2uswKt+7njNzw0OlqZ/O39DwuZcHWPRRkREDVH63WL8eCIdW09nQlVaVWSamcjwYudmGOfvDi8n/p34JKwhDB8/IyKihmn0mhOIu34X/2jniGbWZsj/o9n25yacqrQCz9JVsTL7n7vYHv65kYn2rjYrnUabAhZKeZ3NVrhXXI7NpzKwMS4d2apSAFV34A32booJvZrD19W6TnLUV2zO1TEWbURE1JCVlFfi18Tb2BB7A8nZhdr9PVrYYLy/B/7RzhFyGR8SXx3WEIaPnxERUcO05kgqPtmXXKOxjZVy7V1qTRo92lCrarSZ/GlaadUYWT1ZEqRCrcHBi9lYf/wG4tPva/d3crPGxF7NMaiDE0xY6z2Czbk6xqKNiIio6mlgp9LuYUPcDRy8mAO1pqrMaGplitd6uGNUN1fYNlaKnNKwsIYwfPyMiIgapqKySnx6IBnlaqH6ddrM/nuXW0NqTF24WYB1x9Ow+/xtVKiraj1HSyXG9nDH6O5urPX+hM25OsaijYiISFdWwQP8dCIDm09l4G5xOQBAIZPieZ+mGO/vAR9OgwDAGqI+4GdERET0qNzCUmw6mYEfT2TgTlEZAEAhl2KYrzMm9GyOds78O5PNuTrGoo2IiKh6ZZVq7D2fhQ2xN3DuZoF2v6+rNcb3dMdz3g37yV+sIQwfPyMiIqLHK6tUY9+FLKw7fgPn/1Tr+TW3wcRezfGPdo71ZvpubWNzro6xaCMiInqyxMx8bIi9gb3ns1Cu/u+Tv0Z3d8MYP3c4WTW8J3+xhjB8/IyIiIieTBAEnMm4j3XHb2B/UrZ2eZNm1mYY39MdI7u6waqRicgp6xabc3WMRRsREVHN5RWWYevpqmkQD5/8JZNKMLC9E8b39EA3jyaQSBrGN6ysIQwfPyMiIqKnk1XwAD+eSMemkxm4X1IBADAzkWF452aY2MsDrRwsRE5YN9icq2Ms2oiIiJ5ehVqDyEs5WB97A6fS7mn3t21qifH+7hjq2wxmCuOe8soawvDxMyIiIvp7SivU+DXxFtYdv4Hk7ELt/j6t7TCxlwcC2zhAasRTXtmcq2Ms2oiIiJ7N5SwVfoi7gR1nb6G0omrKq5WZCUZ2c8Vrfu5ws20kckL9YA1h+PgZERERPRtBEHDi+j2sO56GyMs5eNiJam5njvH+7ni5qysaK+XihtQDNufqGIs2IiKi2lFQUoFt8Zn44cQNZN57AACQSID+Xg4Y5++B3q3sjOobVtYQho+fERERUe3JvFeCH+JuYMvpTBSWVgIAGivlGNHVBeP9PeBhZy5ywtrD5lwdY9FGRERUu9QaAdEpudgQl44jV/K0+1vYm2NcD3e81MUFFqb1f1Fh1hCGj58RERFR7Ssuq8QvZ29h/fE0pOYVA6j6QrafpwMm9mqOXq1s6/0axGzO1TEWbURERPqTmleEjXHp+DnhJorKqr5hNVfI8FIXF4zzd6/XiwqzhjB8/IyIiIj0R6MRcPTaHaw/nobDKf/9Qra1Q2NM6OWB4Z1c6u0axGzO1TEWbURERPpXVFaJHWduYkNcOq7lFmn3925lh3H+7ujf1hGyejbllTWE4eNnREREVDeu5xVhQ+wN/JxwE8XlagBVaxCP6u6KsT3c4dKkfq1BzOZcHWPRRkREVHcEQUBs6l2sj72BqMs50PxRzTSzNsNYf3eM7OqKJuYKcUPWEGsIw8fPiIiIqG6pSiuwPf4mNsTeQMa9EgCAVAKEtHfCxF7N0c2jSb2Y8lrTGkJah5mIiIiIaoVEIkGvVnZYO64rYkKDMCWgJawbmeBW/gNE7E9Gj/AovPvzOVy8XSB2VPobVq1aBQ8PD5iamsLPzw+nTp36y/Hbt2+Hl5cXTE1N4e3tjX379mmPVVRUYN68efD29oa5uTmcnZ0xbtw43L59W9+XQURERH+TpakJJvdujsNzA/HtuK7o1coWGgHYn5SNV/4Th+e/Oobt8ZkorVCLHbVW8M65WsJvVImIiMRVWqHGrnO3sSH2Bi7eVmn3d3VvgvE9PTCwgxNMZIb3vSRrCF1bt27FuHHjsHr1avj5+WH58uXYvn07UlJS4ODg8Mj42NhY9O3bF+Hh4Xj++eexadMmLFmyBGfOnEGHDh1QUFCAl19+GW+88QZ8fHxw//59zJgxA2q1GvHx8TXKxM+IiIhIfCnZhVgfewM7zt5EaYUGAGBrrsCrfm54rYc7HC1NRU74KE5rrWMs2oiIiAyDIAg4k3Ef62PTsf9CFir/mPPqYKHEGD93jPZzhYOF4RRvrCF0+fn5oVu3bli5ciUAQKPRwNXVFdOnT8f8+fMfGT9y5EgUFxdjz5492n09evSAr68vVq9eXe17nD59Gt27d0d6ejrc3NweOV5WVoaysjLtzyqVCq6urvyMiIiIDMD94nJsOZ2JjXE3cLugFAAgl0rwnHdTTOzlgU5uTURO+F+c1kpEREQNkkQiQRd3G3w1uhNi5/fDjP6tYW+hRG5hGb74/Qp6RRzCjC1nkZB+H/yO0rCUl5cjISEBwcHB2n1SqRTBwcGIi4ur9jVxcXE64wEgJCTkseMBoKCgABKJBNbW1tUeDw8Ph5WVlXZzdXV9+oshIiIivWhirsBbgS1x5N0gfD2mM7p72KBSI2DXudt48etYDFt1HL8m3kJ5pUbsqDXG5hwREREZLQdLU8z6Rxscn9cPK0b5oot7E1SoBfyaeBsvfROLF1YeN6r1Suq7O3fuQK1Ww9HRUWe/o6MjsrOzq31Ndnb2U40vLS3FvHnzMHr06Md+gx0WFoaCggLtlpmZ+TeuhoiIiPRJLpPiOe+m2DbFH3um98ZLnV2gkEmRmJmPGVsS0XvJIXwVdRV3i8qefDKRsTlHRERERk8hl2KobzP831s9sWd6b4zo4gKFXIoLtwoQ+vN5+IdHYcmBZNzKfyB2VNKjiooKvPLKKxAEAd98881jxymVSlhaWupsREREZLg6NLPC56/44Pj8fpj9jzbaWROfR16Bf8QhzN1u2A8KY3OOiIiIGpQOzazw6QgfnAjrj3kDvdDM2gz3SyrwTXQq+iw5hDc3xiP22h1OeRWBnZ0dZDIZcnJydPbn5OTAycmp2tc4OTnVaPzDxlx6ejoiIyPZcCMiIjJC9hZKvNO/NY7P64flI33h42KF8koNfk64icFfHsMrq+Oq1iRWG9aUVzbniIiIqEGy+WO9kpjQQPxnbBf0bGkLjQAcvJiDV789iQFfHMHGE+koLqsUO2qDoVAo0KVLF0RFRWn3aTQaREVFwd/fv9rX+Pv764wHgMjISJ3xDxtzV69exe+//w5bW1v9XAAREREZBIVcimGdmuHXab3xy9s98YKPM+RSCU7duIe3fjqDgE+jsTomFfkl5WJHBcCntdYaPmmNiIio/ruaU4gNcTfwy5lbKCmvWofOQinHqO6ueO+5tpBIJLX+nqwhdG3duhXjx4/Hf/7zH3Tv3h3Lly/Htm3bkJycDEdHR4wbNw7NmjVDeHg4ACA2NhYBAQGIiIjA4MGDsWXLFnzyySc4c+YMOnTogIqKCrz88ss4c+YM9uzZo7M+nY2NDRQKxRMz8TMiIiKq/7ILSvHjiXRsOpWBe8VVTTlTEymGd3bBoiHtoZDX/v1rNa0h5LX+zkRERET1VGtHC/x7mDfeHeiFn+NvYuOJdKTdKUbGvRK9NOboUSNHjkReXh4++OADZGdnw9fXFwcOHNA21TIyMiCV/rd47tmzJzZt2oT3338f7733Hlq3bo2dO3eiQ4cOAIBbt25h165dAABfX1+d9zp8+DACAwPr5LqIiIhIXE5Wppgb4olp/Vph17nbWHf8Bi5nqZCcpdJLY+5p8M65WsJvVImIiIyPRiPg6LU7sDVXoEMzK728B2sIw8fPiIiIyPgIgoBTafcglUrQzcNGL+/BO+eIiIiInpFUKkFAG3uxYxARERFRLZNIJPBrYRjr0PKBEERERERERERERCJhc46IiIiIiIiIiEgkbM4RERERERERERGJhM05IiIiIiIiIiIikbA5R0REREREREREJBI254iIiIiIiIiIiEQianPuyJEjGDJkCJydnSGRSLBz506d4xMmTIBEItHZBg4cqDPGw8PjkTERERE6Y86fP48+ffrA1NQUrq6uWLp06SNZtm/fDi8vL5iamsLb2xv79u2r9eslIiIiIiIiIiL6M1Gbc8XFxfDx8cGqVaseO2bgwIHIysrSbps3b35kzEcffaQzZvr06dpjKpUKAwYMgLu7OxISEvDpp59i0aJFWLNmjXZMbGwsRo8ejcmTJ+Ps2bMYNmwYhg0bhqSkpNq9YCIiIiIiIiIioj+Ri/nmgwYNwqBBg/5yjFKphJOT01+OsbCweOyYn376CeXl5fj++++hUCjQvn17JCYmYtmyZfjnP/8JAFixYgUGDhyI0NBQAMDixYsRGRmJlStXYvXq1X/jyoiIiIiIiIiIiJ7M4Neci46OhoODAzw9PfHWW2/h7t27j4yJiIiAra0tOnXqhE8//RSVlZXaY3Fxcejbty8UCoV2X0hICFJSUnD//n3tmODgYJ1zhoSEIC4u7rG5ysrKoFKpdDYiIiIiIiIiIqKnIeqdc08ycOBADB8+HM2bN0dqairee+89DBo0CHFxcZDJZACAd955B507d4aNjQ1iY2MRFhaGrKwsLFu2DACQnZ2N5s2b65zX0dFRe6xJkybIzs7W7vvzmOzs7MdmCw8Px4cfflibl0tERERERERERA2MQTfnRo0apf2zt7c3OnbsiJYtWyI6Ohr9+/cHAMyePVs7pmPHjlAoFHjzzTcRHh4OpVKpt2xhYWE6761SqeDq6qq39yMiIiIiIiIiIuNj8NNa/6xFixaws7PDtWvXHjvGz88PlZWVuHHjBgDAyckJOTk5OmMe/vxwnbrHjfmrte6USiUsLS11NiIiIiIiIiIioqdRr5pzN2/exN27d9G0adPHjklMTIRUKoWDgwMAwN/fH0eOHEFFRYV2TGRkJDw9PdGkSRPtmKioKJ3zREZGwt/fXw9XQUREREREREREVEXUaa1FRUU6d8GlpaUhMTERNjY2sLGxwYcffoiXXnoJTk5OSE1NxbvvvotWrVohJCQEQNWDHE6ePImgoCBYWFggLi4Os2bNwmuvvaZtvL366qv48MMPMXnyZMybNw9JSUlYsWIFvvjiC+37zpgxAwEBAfj8888xePBgbNmyBfHx8VizZk2Nr0UQBADggyGIiIjoqTysHR7WEmR4WOcRERHR31HjOk8Q0eHDhwUAj2zjx48XSkpKhAEDBgj29vaCiYmJ4O7uLrzxxhtCdna29vUJCQmCn5+fYGVlJZiamgpt27YVPvnkE6G0tFTnfc6dOyf07t1bUCqVQrNmzYSIiIhHsmzbtk1o06aNoFAohPbt2wt79+59qmvJzMys9lq4cePGjRs3btxqsmVmZv69gor0jnUeN27cuHHjxu1ZtifVeRJB4Ne0tUGj0eD27duwsLCARCKp9fM/fOBEZmZmg1jfjtdr3Hi9xq+hXTOv17jp+3oFQUBhYSGcnZ0hldarFUcaDNZ5tYvXa/wa2jXzeo0br9e4GUqdZ9BPa61PpFIpXFxc9P4+De3hE7xe48brNX4N7Zp5vcZNn9drZWWll/NS7WCdpx+8XuPX0K6Z12vceL3GTew6j1/PEhERERERERERiYTNOSIiIiIiIiIiIpGwOVdPKJVKLFy4EEqlUuwodYLXa9x4vcavoV0zr9e4NbTrpbrX0P4Z4/Uav4Z2zbxe48brNW6Gcr18IAQREREREREREZFIeOccERERERERERGRSNicIyIiIiIiIiIiEgmbc0RERERERERERCJhc46IiIiIiIiIiEgkbM4ZuPDwcHTr1g0WFhZwcHDAsGHDkJKSInYsvfnmm2/QsWNHWFpawtLSEv7+/ti/f7/YsepMREQEJBIJZs6cKXYUvVi0aBEkEonO5uXlJXYsvbp16xZee+012NrawszMDN7e3oiPjxc7ll54eHg88vlKJBJMnTpV7Gh6oVarsWDBAjRv3hxmZmZo2bIlFi9eDGN+zlJhYSFmzpwJd3d3mJmZoWfPnjh9+rTYsWrFkSNHMGTIEDg7O0MikWDnzp06xwVBwAcffICmTZvCzMwMwcHBuHr1qjhhyWiwzmOdZ0xY57HOMyas84yrzgMMv9Zjc87AxcTEYOrUqThx4gQiIyNRUVGBAQMGoLi4WOxoeuHi4oKIiAgkJCQgPj4e/fr1w9ChQ3Hx4kWxo+nd6dOn8Z///AcdO3YUO4petW/fHllZWdrt2LFjYkfSm/v376NXr14wMTHB/v37cenSJXz++edo0qSJ2NH04vTp0zqfbWRkJABgxIgRIifTjyVLluCbb77BypUrcfnyZSxZsgRLly7FV199JXY0vXn99dcRGRmJjRs34sKFCxgwYACCg4Nx69YtsaM9s+LiYvj4+GDVqlXVHl+6dCm+/PJLrF69GidPnoS5uTlCQkJQWlpax0nJmLDOY51nbFjnsc4zFqzzjKvOA+pBrSdQvZKbmysAEGJiYsSOUmeaNGkifPvtt2LH0KvCwkKhdevWQmRkpBAQECDMmDFD7Eh6sXDhQsHHx0fsGHVm3rx5Qu/evcWOIZoZM2YILVu2FDQajdhR9GLw4MHCpEmTdPYNHz5cGDNmjEiJ9KukpESQyWTCnj17dPZ37txZ+Ne//iVSKv0AIOzYsUP7s0ajEZycnIRPP/1Uuy8/P19QKpXC5s2bRUhIxop1nnFinWecWOexzjMmDanOEwTDrPV451w9U1BQAACwsbEROYn+qdVqbNmyBcXFxfD39xc7jl5NnToVgwcPRnBwsNhR9O7q1atwdnZGixYtMGbMGGRkZIgdSW927dqFrl27YsSIEXBwcECnTp2wdu1asWPVifLycvz444+YNGkSJBKJ2HH0omfPnoiKisKVK1cAAOfOncOxY8cwaNAgkZPpR2VlJdRqNUxNTXX2m5mZGfWdEQCQlpaG7OxsnX9HW1lZwc/PD3FxcSImI2PDOs84sc4zTqzzWOcZk4Zc5wGGUevJ6+RdqFZoNBrMnDkTvXr1QocOHcSOozcXLlyAv78/SktL0bhxY+zYsQPt2rUTO5bebNmyBWfOnDGq+fyP4+fnh/Xr18PT0xNZWVn48MMP0adPHyQlJcHCwkLseLXu+vXr+OabbzB79my89957OH36NN555x0oFAqMHz9e7Hh6tXPnTuTn52PChAliR9Gb+fPnQ6VSwcvLCzKZDGq1Gh9//DHGjBkjdjS9sLCwgL+/PxYvXoy2bdvC0dERmzdvRlxcHFq1aiV2PL3Kzs4GADg6Oursd3R01B4jelas84wT6zzWecaIdZ7xach1HmAYtR6bc/XI1KlTkZSUZPSda09PTyQmJqKgoAA///wzxo8fj5iYGKMs3DIzMzFjxgxERkY+8i2FMfrzN00dO3aEn58f3N3dsW3bNkyePFnEZPqh0WjQtWtXfPLJJwCATp06ISkpCatXrzb6ou27777DoEGD4OzsLHYUvdm2bRt++uknbNq0Ce3bt0diYiJmzpwJZ2dno/18N27ciEmTJqFZs2aQyWTo3LkzRo8ejYSEBLGjEdV7rPNY59V3rPNY5xkT1nms8+oap7XWE9OmTcOePXtw+PBhuLi4iB1HrxQKBVq1aoUuXbogPDwcPj4+WLFihdix9CIhIQG5ubno3Lkz5HI55HI5YmJi8OWXX0Iul0OtVosdUa+sra3Rpk0bXLt2TewoetG0adNH/mOjbdu2Rj3FAwDS09Px+++/4/XXXxc7il6FhoZi/vz5GDVqFLy9vTF27FjMmjUL4eHhYkfTm5YtWyImJgZFRUXIzMzEqVOnUFFRgRYtWogdTa+cnJwAADk5OTr7c3JytMeIngXrPNZ5xoh1nnFincc6zxgZQq3H5pyBEwQB06ZNw44dO3Do0CE0b95c7Eh1TqPRoKysTOwYetG/f39cuHABiYmJ2q1r164YM2YMEhMTIZPJxI6oV0VFRUhNTUXTpk3FjqIXvXr1QkpKis6+K1euwN3dXaREdWPdunVwcHDA4MGDxY6iVyUlJZBKdf8alclk0Gg0IiWqO+bm5mjatCnu37+PgwcPYujQoWJH0qvmzZvDyckJUVFR2n0qlQonT540+rWySL9Y57HOM2as84wT6zzWecbIEGo9Tms1cFOnTsWmTZvw66+/wsLCQjvf2crKCmZmZiKnq31hYWEYNGgQ3NzcUFhYiE2bNiE6OhoHDx4UO5peWFhYPLKujLm5OWxtbY1yvZm5c+diyJAhcHd3x+3bt7Fw4ULIZDKMHj1a7Gh6MWvWLPTs2ROffPIJXnnlFZw6dQpr1qzBmjVrxI6mNxqNBuvWrcP48eMhlxv3XzFDhgzBxx9/DDc3N7Rv3x5nz57FsmXLMGnSJLGj6c3BgwchCAI8PT1x7do1hIaGwsvLCxMnThQ72jMrKirSubsjLS0NiYmJsLGxgZubG2bOnIl///vfaN26NZo3b44FCxbA2dkZw4YNEy801Xus81jnGRPWeazzjAnrPOOq84B6UOvVyTNh6W8DUO22bt06saPpxaRJkwR3d3dBoVAI9vb2Qv/+/YXffvtN7Fh1KiAgQJgxY4bYMfRi5MiRQtOmTQWFQiE0a9ZMGDlypHDt2jWxY+nV7t27hQ4dOghKpVLw8vIS1qxZI3YkvTp48KAAQEhJSRE7it6pVCphxowZgpubm2Bqaiq0aNFC+Ne//iWUlZWJHU1vtm7dKrRo0UJQKBSCk5OTMHXqVCE/P1/sWLXi8OHD1f59O378eEEQBEGj0QgLFiwQHB0dBaVSKfTv379B/HNO+sU6j3WeMWGdxzrPmLDOM646TxAMv9aTCIIg1E0bkIiIiIiIiIiIiP6Ma84RERERERERERGJhM05IiIiIiIiIiIikbA5R0REREREREREJBI254iIiIiIiIiIiETC5hwREREREREREZFI2JwjIiIiIiIiIiISCZtzREREREREREREImFzjoiIiIiIiIiISCRszhFRg3Pjxg1IJBIkJiaKHUUrOTkZPXr0gKmpKXx9fZ/69YZ4TURERER1zRBrItZ5RPQkbM4RUZ2bMGECJBIJIiIidPbv3LkTEolEpFTiWrhwIczNzZGSkoKoqCix42D9+vWwtrYWOwYRERHVM6zzHsU6j4iehM05IhKFqakplixZgvv374sdpdaUl5f/7dempqaid+/ecHd3h62tbS2mEpdarYZGoxE7BhEREdUh1nm6WOcR0ZOwOUdEoggODoaTkxPCw8MfO2bRokWP3Pq/fPlyeHh4aH+eMGEChg0bhk8++QSOjo6wtrbGRx99hMrKSoSGhsLGxgYuLi5Yt27dI+dPTk5Gz549YWpqig4dOiAmJkbneFJSEgYNGoTGjRvD0dERY8eOxZ07d7THAwMDMW3aNMycORN2dnYICQmp9jo0Gg0++ugjuLi4QKlUwtfXFwcOHNAel0gkSEhIwEcffQSJRIJFixY99jxLly5Fq1atoFQq4ebmho8//rjasdV9I/q/31ifO3cOQUFBsLCwgKWlJbp06YL4+HhER0dj4sSJKCgogEQi0clUVlaGuXPnolmzZjA3N4efnx+io6Mfed9du3ahXbt2UCqVyMjIqDYjERERGSfWeazziOjpsDlHRKKQyWT45JNP8NVXX+HmzZvPdK5Dhw7h9u3bOHLkCJYtW4aFCxfi+eefR5MmTXDy5ElMmTIFb7755iPvExoaijlz5uDs2bPw9/fHkCFDcPfuXQBAfn4++vXrh06dOiE+Ph4HDhxATk4OXnnlFZ1zbNiwAQqFAsePH8fq1aurzbdixQp8/vnn+Oyzz3D+/HmEhITghRdewNWrVwEAWVlZaN++PebMmYOsrCzMnTu32vOEhYUhIiICCxYswKVLl7Bp0yY4Ojr+7d/bmDFj4OLigtOnTyMhIQHz58+HiYkJevbsieXLl8PS0hJZWVk6maZNm4a4uDhs2bIF58+fx4gRIzBw4EDttQBASUkJlixZgm+//RYXL16Eg4PD385IRERE9Q/rPNZ5RPSUBCKiOjZ+/Hhh6NChgiAIQo8ePYRJkyYJgiAIO3bsEP78r6WFCxcKPj4+Oq/94osvBHd3d51zubu7C2q1WrvP09NT6NOnj/bnyspKwdzcXNi8ebMgCIKQlpYmABAiIiK0YyoqKgQXFxdhyZIlgiAIwuLFi4UBAwbovHdmZqYAQEhJSREEQRACAgKETp06PfF6nZ2dhY8//lhnX7du3YS3335b+7OPj4+wcOHCx55DpVIJSqVSWLt2bbXHH17T2bNnBUEQhHXr1glWVlY6Y/7392thYSGsX7++2vNV9/r09HRBJpMJt27d0tnfv39/ISwsTPs6AEJiYuJjr4WIiIiMF+s81nlE9PTk4rQEiYiqLFmyBP369Xvst4g10b59e0il/70R2NHRER06dND+LJPJYGtri9zcXJ3X+fv7a/8sl8vRtWtXXL58GUDVVIDDhw+jcePGj7xfamoq2rRpAwDo0qXLX2ZTqVS4ffs2evXqpbO/V69eOHfuXA2vELh8+TLKysrQv3//Gr/mSWbPno3XX38dGzduRHBwMEaMGIGWLVs+dvyFCxegVqu11/5QWVmZzvopCoUCHTt2rLWcREREVD+xzqsZ1nlExOYcEYmqb9++CAkJQVhYGCZMmKBzTCqVQhAEnX0VFRWPnMPExETnZ4lEUu2+p1mwtqioCEOGDMGSJUseOda0aVPtn83NzWt8zmdhZmb2VONr8rtbtGgRXn31Vezduxf79+/HwoULsWXLFrz44ovVnrOoqAgymQwJCQmQyWQ6x/5c3JqZmTXYp7ERERHRf7HOqxnWeUTENeeISHQRERHYvXs34uLidPbb29sjOztbp/hITEystfc9ceKE9s+VlZVISEhA27ZtAQCdO3fGxYsX4eHhgVatWulsT1OoWVpawtnZGcePH9fZf/z4cbRr167G52ndujXMzMwQFRVVo/H29vYoLCxEcXGxdl91v7s2bdpg1qxZ+O233zB8+HDtgsoKhQJqtVpnbKdOnaBWq5Gbm/vI78TJyanG10JEREQNB+u8J2OdR0RszhGR6Ly9vTFmzBh8+eWXOvsDAwORl5eHpUuXIjU1FatWrcL+/ftr7X1XrVqFHTt2IDk5GVOnTsX9+/cxadIkAMDUqVNx7949jB49GqdPn0ZqaioOHjyIiRMnPlLMPEloaCiWLFmCrVu3IiUlBfPnz0diYiJmzJhR43OYmppi3rx5ePfdd/HDDz8gNTUVJ06cwHfffVfteD8/PzRq1AjvvfceUlNTsWnTJqxfv157/MGDB5g2bRqio6ORnp6O48eP4/Tp09qi1cPDA0VFRYiKisKdO3dQUlKCNm3aYMyYMRg3bhx++eUXpKWl4dSpUwgPD8fevXuf6ndCREREDQPrvCdjnUdEbM4RkUH46KOPHpmO0LZtW3z99ddYtWoVfHx8cOrUqWdas+R/RUREICIiAj4+Pjh27Bh27doFOzs7ANB+C6pWqzFgwAB4e3tj5syZsLa21ln3pCbeeecdzJ49G3PmzIG3tzcOHDiAXbt2oXXr1k91ngULFmDOnDn44IMP0LZtW4wcOfKR9VUesrGxwY8//oh9+/bB29sbmzdvxqJFi7THZTIZ7t69i3HjxqFNmzZ45ZVXMGjQIHz44YcAgJ49e2LKlCkYOXIk7O3tsXTpUgDAunXrMG7cOMyZMweenp4YNmwYTp8+DTc3t6e6FiIiImo4WOc9Ges8ooZNIvzvZHUiIiIiIiIiIiKqE7xzjoiIiIiIiIiISCRszhEREREREREREYmEzTkiIiIiIiIiIiKRsDlHREREREREREQkEjbniIiIiIiIiIiIRMLmHBERERERERERkUjYnCMiIiIiIiIiIhIJm3NEREREREREREQiYXOOiIiIiIiIiIhIJGzOERERERERERERiYTNOSIiIiIiIiIiIpH8P/AzZjPCm3A7AAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 寻找英文招聘文本最佳聚类数量值\n",
    "en_to_vec.elbow_method_optimized()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-22T08:33:41.894039Z",
     "start_time": "2024-03-22T08:33:27.863530700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存文件中~ ../Data/文本向量化/英文文本.gexf\n"
     ]
    }
   ],
   "source": [
    "n_clusters1 = 6\n",
    "en_to_vec.cluster_words_optimized(n_clusters=n_clusters1)\n",
    "save_path = \"../Data/文本向量化/\"\n",
    "en_to_vec.save_node_edge_optimized(save_path+\"英文文本\", similarity_threshold=0.8)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-22T08:33:49.424221900Z",
     "start_time": "2024-03-22T08:33:41.826373300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1500x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 寻找中文招聘文本最佳聚类数量值\n",
    "zh_to_vec.elbow_method_optimized()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T16:40:01.581709400Z",
     "start_time": "2024-03-19T16:39:51.439166600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\tools_installed\\anaconda\\envs\\bishe\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存文件中~ ../Data/文本向量化/中文文本.gexf\n"
     ]
    }
   ],
   "source": [
    "n_clusters1 = 5\n",
    "zh_to_vec.cluster_words_optimized(n_clusters=n_clusters1)\n",
    "save_path = \"../Data/文本向量化/\"\n",
    "zh_to_vec.save_node_edge_optimized(save_path+\"中文文本\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T16:41:10.189559500Z",
     "start_time": "2024-03-19T16:40:01.556820400Z"
    }
   }
  }
 ]
}
